Development Of Modeling Approach Research Articles

Child-friendly school-based learning management model for health and physical education

The development of learning management models is crucial to address the diverse needs of effective learning while promoting active participation, preventing discrimination, and safeguarding students' rights and responsibilities in line with their growth. This article aims to create a child-friendly school-based learning model for health and physical education in elementary schools and validate it through expert assessments. Following Brog and Gall's model development approach, data collection involved assessment questionnaires administered to various stakeholders, including school supervisors, principals, teachers, students, and committees. The findings underscore the importance of incorporating diverse learning methods, adequate infrastructure, gender sensitivity, and meaningful learning experiences into the model. The three sequential stages' assessment results demonstrate the proposed model's high feasibility, scoring consistently high across stages. Referred to as a model, this innovative model showcases significant promise, suggesting further refinement to ensure its efficacy in addressing multifaceted learning requirements, fostering inclusive participation, and upholding students' rights and obligations per their developmental stage. This study highlights the need for evolving learning management models to cater to diverse educational needs effectively.

Simultaneous Water Sorption and Crystallization in ASDs 2: Modeling Long-Term Stabilities.

The physical stability of amorphous solid dispersions (ASDs) is a major topic in the formulation research of oral dosage forms. To minimize the effort of investigating the long-term stability using cost- and time-consuming experiments, we developed a thermodynamic and kinetic modeling framework to predict and understand the crystallization kinetics of ASDs during long-term storage below the glass transition. Since crystallization of the active phrarmaceutical ingredients (APIs) in ASDs largely depends on the amount of water absorbed by the ASDs, water-sorption kinetics and API-crystallization kinetics were considered simultaneously. The developed modeling approach allows prediction of the time evolution of viscosity, supersaturation, and crystallinity as a function of drug load, relative humidity, and temperature. It was applied and evaluated against two-year-lasting crystallization experiments of ASDs containing nifedipine and copovidone or HPMCAS measured in part I of this work. We could show that the proposed modeling approach is able to describe the interplay between water sorption and API crystallization and to predict long-term stabilities of ASDs just based on short-term measurements. Most importantly, it enables explaining and understanding the reasons for different and sometimes even unexpected crystallization behaviors of ASDs.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m 2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using Pmetrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m 2 (range: 40-70.3 kg/m 2 ). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Development of SAC Media Integrated with Project Based Learning Model on Reaction Rate Material

This research aims to determine the analysis of the feasibility level and response of students to SAC (Smart Apps Creator) media integrated with project based learning on reaction rate material. The research method used is Research and Development with a 4D development model approach (define, design, development and disseminate). Research data collection is carried out by interviews and questionnaires or questionnaires that are distributed to teachers and students. This research was conducted at SMAS PAB 8 Saentis. The population and samples in this study are chemistry lecturers, teachers and students of grade XI 2 for the 2023/2024 academic year taken by purposive sampling of 30 people. Based on validity, it shows that the SAC learning media developed was declared valid by validators with the criteria of "Very Feasible" with an average validation score of 89.86%, then based on the results of student responses, it was obtained that the SAC learning media developed as a whole received an average percentage result of 83.81% with the criteria of "Strongly Agree". Therefore, SAC learning media integrated with the project-based learning model on the reaction rate material is suitable for use as a learning medium.

PENGEMBANGAN KOMIK EDUKASI BERBASIS CREATIVE PROBLEM SOLVING BERBANTUAN KVISOFT FLIPBOOK MAKER PRO UNTUK SEKOLAH DASAR

This research aims to develop and produce the effectiveness of an educational comic based on Creative Problem Solving (CPS) using Kvisoft Flipbook Maker Pro as interactive media for elementary school students. This research uses the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model development approach. The main innovation of this research is the use of the Creative Problem Solving approach in designing educational comic content. In addition, Kvisoft Flipbook Maker Pro is used to present comics interactively, adding a more interesting visual dimension. The research results include educational comics developed and evaluation results from field trials. Evaluation involves responses from students, teachers, and the appropriateness, attractiveness, and effectiveness of comics in increasing understanding of educational concepts. It is hoped that this research can contribute to the development of innovative teaching methods and increase student involvement in the learning process. The results obtained were that the teaching materials developed obtained a validity percentage of 89.06% (Content Validation), 80% (Graphic Validation), and 83.3% (Language Validation). The practicality of the teacher's response was obtained at 3.7 (very practical), while the student's response was 3.5 (very practical. Effectiveness can be seen from student learning outcomes, namely an N-Gain Score of 0.71 (High) in the quite effective category. This means, The teaching materials developed are valid, practical and effective for grade 3 elementary school students

Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach

Aerosol jet printing (AJP) is a newly-developed technology for printed electronics in additive manufacturing (AM) industry. Compared to conventional printed electronics, AJP technology has the capability of fabricating high-resolution patterns (∼ 10 µm) with multiple materials and high degrees of freedom. However, insufficiently optimizing the printing process, including reliance on traditional trial-and-error methods and theoretical models, may lead to a decline in production quality, consequently limiting the widespread adoption of AJP technology. In this paper, an integrated machine learning approach is developed for autonomous optimization of process parameters and in-situ anomaly detection in AJP. In the developed approach, a coaxial camera is incorporated with a stepwise data-driven modeling approach for optimal operating window identification and process model development of AJP, thereby systematically optimizing the main process parameters prior to printing. Subsequently, a convolutional neural network (CNN) model is developed and integrated with an alignment camera for in-situ process monitoring and printing status evaluation, enabling the anomaly detection of the AJP process. Afterwards, leveraging process similarity through transfer learning allows rapid calibration of detected system drifts or variations in process stability, thereby enhancing the product consistency of AJP. Generally, by utilizing state-of-the-art machine learning techniques, the autonomous optimization of process parameters and anomaly detection in AJP achieve accuracies of 95.3% and 92.7%, respectively. This indicates the effectiveness and systematic nature of the developed approach compared to traditional trial-and-error methods and theoretical models. Furthermore, due to its data-driven characteristics, this approach has the potential to extend its applicability to other noncontact ink writing techniques, facilitating further research for process optimization in AM.

Development Of Reading Materials Based On Local Wisdom To Support Literacy Activities In Elementary School

This research is related to the feasibility, effectiveness and practicality of developing Local Wisdom-Based Reading Materials to Support Class IV Literacy Activities at SD Negeri 1 Fajar City, South Aceh. This research is development research, namely the 4D model development approach (Four-D model) developed by Thiagarajan. This research was carried out at SD Negeri 1 Kota Fajar. This research was conducted in the even semester of the 2022/2023 academic year. The time for this research is planned to be carried out from April 2023 to June 2023. The instruments prepared by the researchers are to determine the Validity, Practicality and Effectiveness of the reading material products created. Research and study of the development process with the 4D model resulted in the conclusion that based on the recapitulation from the Material Validator Expert Team, a percentage of 87.5% was obtained, media experts obtained a percentage of 84% and language experts obtained a percentage of 90%, so the reading material developed by the researcher met the criteria. validity so that it is declared very feasible. Based on the results of the increase in pretest and posttest scores of 27.84, namely from 55.10 and the average posttest score was 82.94, the local wisdom-based reading materials developed can be declared effective in improving student learning outcomes. Furthermore, based on a questionnaire designed to analyze user and teacher reactions to local wisdom-based reading material media to test user practicality, the score obtained was 27 out of a maximum total score of 30 with a percentage of 90%. This means that the practicality of data analysis is very good and can be applied to the learning process.

Peridynamic analysis of thermomechanical effects in fracture toughening mechanisms of stop holes

In this study, a bond-based fully coupled thermomechanical peridynamic framework is implemented to study the fracture toughening of stop-holes in brittle materials subjected to thermomechanical loads. The developed modelling approach aims to provide comprehensive guidance about thermal effects on the fracture toughening mechanism of inclusions under realistic engineering conditions. First, the model is verified using simulation of several benchmark case studies and predicts the mechanical, thermal and damage parameters with high accuracy. Then, a fully coupled thermomechanical analysis of a 2D structure is performed to study the effect of the location of a single stop-hole on fracture toughening. A thorough investigation of the arresting and accelerating effect of the stop-hole is conducted. Additionally, the accuracy of various degrees of thermomechanical coupling including one-way weak coupling and no coupling is compared with full coupling. The study of crack propagation dynamics reveals that ignoring thermal effects underpredicts the toughness enhancement of stop-holes. Furthermore, although the weak thermomechanical coupling offers a reasonable prediction of crack propagation dynamics in most cases of our study, it is not accurate in certain cases. In the final section, some benchmark cases of complex arrangements of multiple stop-holes in literature are revisited. Our results indicate a different toughness trend for some cases when considering the full thermomechanical coupling. This suggests the proposed framework provides a more accurate tool for designing toughened structures using inclusions.

Performance investigation of highway bridge pier columns under the sequential effects of fire followed by vehicle collision and subsequent air blast: A numerical investigation

Historically, it has been demonstrated that bridges may be vulnerable to fire, and in many circumstances, resulting damage might not be apparent, and bridges could maintain acceptable levels of serviceability. In the absence of proven assessment tools and given the limited research that addresses bridge fire, research that better understands response and strives to improve highway bridge resiliency to fire is needed. Extending the work carried out during an earlier research stage, the present study focused on investigating performance of bridge pier columns that survive fire under coupled vehicle collision and air blast. Numerical models of single reinforced concrete columns supported by a pile foundation system and surrounded by air and soil volumes were created using LS-DYNA. As explicit solvers such as those available in LS-DYNA are infrequently used for fire analysis, an indirect two-step approach that integrated heat transfer and structural analyses was developed and validated against published fire-induced impact and blast test results. A parametric study that examined the effects of various fire exposure conditions and column diameters was completed. Performance was comprehensively assessed based on various structural response parameters, which included failure modes, lateral displacement, residual axial capacities, and shear demand-to-capacity ratios. Column damage was then categorized into six levels to qualitatively assess column performance under the aforementioned multi-hazards. The developed modeling approach was shown to be viable, and results indicated that larger column diameters could potentially remain in service in their final damage states after being repaired for fire durations of less than 120 min.

Standardized approach for accurate and reliable model development of ion-exchange chromatography based on parameter-by-parameter method and consideration of extra-column effects.

Developing an accurate and reliable model for chromatographic separation that meets regulatory requirements and ensures consistency in model development remains challenging. In order to address this challenge, a standardized approach was proposed in this study with ion-exchange chromatography (IEC). The approach includes the following steps: liquid flow identification, system and column-specific parameters determination and validation, multi-component system identification, protein amount validation, steric mass action parameters determination and evaluation, and validation of the calibrated model's generalization ability. The parameter-by-parameter (PbP) calibration method and the consideration of extra-column effects were integrated to enhance the accuracy of the developed models. The experiments designed for implementing the PbP method (five gradient experiments for model calibration and one stepwise experiment for model validation) not only streamline the experimental workload but also ensure the extrapolation abilities of the model. The effectiveness of the standardized approach is successfully validated through an application about the IEC separation of industrial antibody variants, and satisfactory results were observed with R2 ≈ 0.9 for the majority of calibration and validation experiments. The standardized approach proposed in this work contributes significantly to improve the accuracy and reliability of the developed IEC models. Models developed using this standardized approach are ready to be applied to a broader range of industrial separation systems, and are likely find further applications in model-assisted decision-making of process development.

Effect of Soil Properties and Powertrain Configuration on the Energy Consumption of Wheeled Electric Agricultural Robots

Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management.

An Entrepreneurial Skills Model for Project-Based Learning in Programming

This research aims to: 1) analyse the development needs of the Project Based-Learning based Entrepreneurial Skills Programming model; 2) design a model that supports the integration of entrepreneurial skills into programming learning; 3) assess the validity of the Project Based-Learning based Entrepreneurial Skills Programming model; 4) pilot the model to evaluate the effectiveness of teaching and developing entrepreneurial skills; 5) examine the effectiveness of the Entrepreneurial Skills Programming model; and 6) identify student responses to the Entrepreneurial Skills Programming model. These skills are important to equip students with relevant and appropriate abilities for the dynamic world of work and rapid technological development. The method used is Research and Development (R&D) with a 4D development model approach: Define, Design, Develop, and Disseminate. Data were collected through literature studies, field surveys, and in-depth interviews with stakeholders. The research stages included needs identification, model design, prototype development, and model dissemination. The validity and reliability of the instruments were tested using the Validity Coefficient (Va) and percentage of agreement (PA). The results showed that the developed entrepreneurship model was effective in improving students' entrepreneurial skills, evidenced by the significant improvement in students' learning outcomes and their positive response to the model. The model is considered to add value and increase students' enthusiasm for learning. This confirms the importance of integrating entrepreneurial skills in programming learning to prepare students for the challenges of today's world of work.

Indonesian multilabel classification using IndoBERT embedding and MBERT classification

The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thus, a model is needed to classify comments, especially toxic ones, in Indonesian. Transformer-based model development and natural language processing approaches can be applied to create classification models. Some previous research related to the classification of toxic comments has been done, but the classification results of the model still require exploration to get optimal results. So, this research uses the proposed model by using different pre-trained models at the embedding and classification stages, in the embedding stage using Indonesia bidirectional encoder representations from transformers (IndoBERT), and classification using multilingual bidirectional encoder representations from transformers (MBERT). The proposed model provides optimal results with an F1 value of 0.9032.

Mechanistic population models for ecological risk assessment and decision support: The importance of good conceptual model diagrams.

The use of mechanistic population models as research and decision-support tools in ecology and ecological risk assessment (ERA) is increasing. This growth has been facilitated by advances in technology, allowing the simulation of more complex systems, as well as by standardized approaches for model development, documentation, and evaluation. Mechanistic population models are particularly useful for simulating complex systems, but the required model complexity can make them challenging to communicate. Conceptual diagrams that summarize key model elements, as well as elements that were considered but not included, can facilitate communication and understanding of models and increase their acceptance as decision-support tools. Currently, however, there are no consistent standards for creating or presenting conceptual model diagrams (CMDs), and both terminology and content vary widely. Here, we argue that greater consistency in CMD development and presentation is an important component of good modeling practice, and we provide recommendations, examples, and a free web app (pop-cmd.com) for achieving this for population models used for decision support in ERAs. Integr Environ Assess Manag 2024;20:1566-1574. © 2023 SETAC.

Educational Comics Based on Creative Problem Solving With the Aid of Kvisoft Flipbook Maker Pro for Elementary School

This research aims to develop and evaluate the effectiveness of using educational comics based on Creative Problem Solving (CPS) with the help of Kvisoft Flipbook Maker Pro as an interactive learning tool for elementary school students. This research adopts the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model development approach. Initial analysis was carried out to identify student needs and elementary school characteristics. Then, educational comics were developed by combining the CPS concept and Kvisoft Flipbook Maker Pro technology. Field trials were conducted to evaluate its effectiveness. The innovation of this research lies in the use of a combination of the CPS approach and interactive flipbook technology. This educational comic not only aims to convey information, but also stimulates students' creativity and problem-solving abilities through interactivity. The research results include the development of educational comics that are integrated with Kvisoft Flipbook Maker Pro. Evaluation is carried out through measuring increased student understanding, active involvement in the learning process, and positive responses from teachers and students. This research is expected to contribute to curriculum development literature by integrating creative and technological approaches in learning. In addition, it can provide new insights into the use of flipbook technology in the context of elementary education.

Network science and explainable AI-based life cycle management of sustainability models.

Model-based assessment of the potential impacts of variables on the Sustainable Development Goals (SDGs) can bring great additional information about possible policy intervention points. In the context of sustainability planning, machine learning techniques can provide data-driven solutions throughout the modeling life cycle. In a changing environment, existing models must be continuously reviewed and developed for effective decision support. Thus, we propose to use the Machine Learning Operations (MLOps) life cycle framework. A novel approach for model identification and development is introduced, which involves utilizing the Shapley value to determine the individual direct and indirect contributions of each variable towards the output, as well as network analysis to identify key drivers and support the identification and validation of possible policy intervention points. The applicability of the methods is demonstrated through a case study of the Hungarian water model developed by the Global Green Growth Institute. Based on the model exploration of the case of water efficiency and water stress (in the examined period for the SDG 6.4.1 & 6.4.2) SDG indicators, water reuse and water circularity offer a more effective intervention option than pricing and the use of internal or external renewable water resources.

Robust Generation of ASC Spheroids for Use as 3D Cultures and in Bioprinted Tissue Models.

Three-dimensional (3D) cell culture techniques have become a valuable tool to mimic the complex interactions of cells with each other and their surrounding extracellular matrix as they occur in vivo. In this respect, 3D spheroids are widely acknowledged as self-assembled cellular aggregates that can be generated from a variety of cell types without the need for exogenous material while being highly reproducible, easy to handle, and cost-effective. Furthermore, due to their capacity to be developed into microtissues, spheroids represent potential building blocks for various tissue engineering applications, including 3D bioprinting approaches for tissue model development. Adipose-derived stromal/stem cells (ASCs), due to their ease of isolation, multipotent nature, and secretory capacity, represent an attractive cell source employed in numerous tissue engineering studies and other cell-based therapy approaches. In this chapter, we describe two procedures for robust spheroid generation, namely the liquid overlay technique, either using agarose-coated 96-well plates or employing agarose-cast micromolds. Furthermore, we show, in principle, the generation of ASC spheroids with subsequent adipogenic differentiation and the spheroid generation using adipogenically differentiated ASCs, as well as the morphological characterization of generated spheroids.

ICT Team Dual-Innovations in the Microlevel of Circular Supply Chain Management: Explicit-Tacit Knowledge, Exchange Ideology, and Leadership Support

An emerging information communication technology (ICT) triggers circular supply chain management (CSCM), there is still much to be addressed effectively, especially at the microlevel of CSCM. The purpose of this article is to build an integrated model for ICT team exploratory and exploitative innovation in the lens of CSCM and circular economy context. Our analysis was supported by 124 industries-ICT teams (368 participants) in Indonesia. We used structural equational model SmartPLS to validate the development model and Sobel approach to confirm the mediation hypothesis. Supply chain manager, ICT department, and industries executive level are suggested to encourage knowledge sharing practices both explicit and tacit knowledge. Firm's supply chain may follow path-goal leaders to promote effectively circular supply chain management. It is also highlighted that exchange ideology as a moderator help leader to understand their team orientation and contribution to knowledge sharing activities and leader's characteristics. We contribute to a microlevel of CSCM context and new understanding of how explicit-tacit knowledge sharing (mediator), exchange ideology (moderator), and leaders support in ICT team exploratory-exploitative innovation, and as new foundation for future studies on cross-CSCM ICT team including encourage more detailed analysis of exchange ideology orientation.

XAI-PDF: A Robust Framework for Malicious PDF Detection Leveraging SHAP-Based Feature Engineering

With the increasing number of malicious PDF files used for cyberattacks, it is essential to develop efficient and accurate classifiers to detect and prevent these threats. Machine Learning (ML) models have successfully detected malicious PDF files. This paper presents XAI-PDF, an efficient system for malicious PDF detection designed to enhance accuracy and minimize decision-making time on a modern dataset, the Evasive-PDFMal2022 dataset. The proposed method optimizes malicious PDF classifier performance by employing feature engineering guided by Shapley Additive Explanations (SHAP). Particularly, the model development approach comprises four phases: data preparation, model building, explainability of the models, and derived features. Utilizing the interpretability of SHAP values, crucial features are identified, and new ones are generated, resulting in an improved classification model that showcases the effectiveness of interpretable AI techniques in enhancing model performance. Various interpretable ML models were implemented, with the Lightweight Gradient Boosting Machine (LGBM) outperforming other classifiers. The Explainable Artificial Intelligence (XAI) global surrogate model generated explanations for LGBM predictions. Experimental comparisons of XAI-PDF with baseline methods revealed its superiority in achieving higher accuracy, precision, and F1-scores with minimal False Positive (FP) and False Negative (FN) rates (99.9%, 100%, 99.89%,0.000, and 0.002, respectively). Additionally, XAI-PDF requires only 1.36 milliseconds per record for predictions, demonstrating increased resilience in detecting evasive malicious PDF files compared to state-of-the-art methods

A Generalized Circuit Model Development Approach With Short Open Thru (SOT) De-Embedding Technique and Its Applications

A Generalized Circuit Model Development Approach With Short Open Thru (SOT) De-Embedding Technique and Its Applications