Development Of Modeling Approach Research Articles

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

Effect of IOT-Based Climate Smart Agriculture Learning Media on Students in Agro Climate Course

Research & Development (R&D) with the Design Science Research Methodology (DSRM) development model approach which aims to (1) Problem Identification and Motivation; (2) Define the Objectives for a Solution; (3) Design and Development; (4) Demonstration; (5) Evaluation; and, (6) Communication. The research locus was at the Gowa Agricultural Development Polytechnic (Polbangtan), Department of Agriculture, D4 Sustainable Agricultural Extension Study Program. Observation, interviews and questionnaires are used in data collection methods to develop climate smart agriculture learning media in Agro climate courses. The data analysis method uses the validity analysis approach, reliability analysis, practicality analysis and effectiveness analysis in the effectiveness analysis using normality and homogeneity tests. The results of the research obtained are (1) in the form of student characteristics, identification of needs and detailed task analysis based on the needs of learning media, to the formulation of problems in the field. This means that what is produced at this stage is also input for the next stage; (2) analysis is carried out by applying methods to simplify the series of activities carried out and integrated according to needs; (3) this stage is carried out making design and development plans as a solution to the problem of producing prototypes that contain trainers for learning agricultural agroclimatology. Based on the testing of media experts and material experts, it is suggested that the media developed is very valid and feasible to implement; (4) the results of the calculation of practicality show that the media is very practical and effective for use in learning Agricultural Agroclimatology courses, this is based on the recapitulation of the assessment of the results of the small group trial, it can be calculated the overall assessment results in the form of an average of 4.65 and a large group average of 4.71; (5) based on the results and paired samples correlations, the sig value (probability value) of 0.000 is smaller than the probability of 0.05, then (t) difference in test results between the experimental class and the control class on the Post Test results, due to the significant difference in average scores, can be declared effective in increasing student competence in the course.

A bi-objective optimization model of metro trains considering energy conservation and passenger waiting time

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach.

Development of Game Based basic Speed Running Movement Training for Children Age 7 - 9 Years

The aim of this research is to develop basic sprint movement training for 7-9 year olds, and to determine the effectiveness of the model developed. The method in this research is the Research & Development model approach from Borg and Gall. The aim of this research is to develop a game-based basic movement training model for sprinting for ages 7-9 years. The small group trial consisted of 20 people, while the large group trial consisted of 30 people. Data collection in this research is by reviewing various literature or literature studies related to the model concept that will be developed according to the product to be made and referring to the needs of analysis, expert reviews and field trials. To calculate effectiveness, the t-test procedure is used. The research results of the game-based basic movement training model for sprinting are appropriate to the characteristics of children aged 7-9 years and are effective for improving sprinting.

Optimalisasi AUM (Amal Usaha Muhammadiyah) Pada Pimpinan Cabang Muhammadiyah Kelayu

The branches manager or Pimpimpinan Cabang Kelayu was one of the Muhammadiyah PCMs in the East Lombok Region. The implementation of Muhammadiyah Branch activities experienced many obstacles so many activities were not carried out according to what was programmed. This is the background for holding optimization activities at PCM Kelayu so that it continues to provide benefits to the community, especially at PCM Kelayu. This research aims to find out how effective the Leadership and Community of Muhammadiyah Kelayu are in carrying out the activities that have been programmed. This activity was carried out using a community development model approach, namely an effort to prepare the community along with steps to strengthen community institutions so that they can realize progress, independence and prosperity in an atmosphere of sustainable social justice through the Real Work Lecture activities of the Muhammadiyah Selong Institute of Social and Health Technology. Based on the problems that have been identified, the solution offered in the KKN-PCM program is assistance in collecting data on Muhammadiyah residents and optimizing the renewal of several AUM facilities.

An Actor-Oriented and Architecture-Driven Approach for Spatially Explicit Agent-Based Modeling

Nowadays, there is an increasing need to rapidly build more realistic models to solve environmental problems in an interdisciplinary context. In particular, agent-based and spatial modeling have proven to be useful for understanding land use and land cover change processes. Both approaches include simulation platforms often used in several research domains to develop models explaining and analyzing complex phenomena. Domain experts generally use an ad hoc approach for model development, which relies on a code-and-fix life cycle, going from a prototype model through progressive refinement. This adaptive approach does not capture systematically actors’ knowledge and their interactions with the environment. The development and maintenance of resulting models become cumbersome and time-consuming. In this article, we propose an actor and architecture-driven approach that relies on relevant existing methods and satisfies the needs of spatially explicit agent-based modeling and implementation. We have designed an Agent Global Experiment framework incorporating a meta-model built from actor, agent architecture, and spatial concepts to produce an initial model from specifications provided by domain experts and system analysts. An engine is built as a tool to support model transformation. Domain knowledge including spatial specifications is summarized in a class diagram which is later transformed into the agent-based model. Finally, the XML file representing the model produced is used as input in the transformation process leading to code. This approach is illustrated on a hunting and population dynamic model to generate a running code for GAMA, an agent-based and spatially explicit simulation platform.

A Comparison Study on Multiple Modeling Approaches for Air Pollutant Geographic Model Development in Shanghai

Geostatistical models have been widely used in the exposure assessment of ambient air pollutants. However, few studies have focused on comparisons of modeling approaches and their prediction results. Here, we collected the NO2 and PM2.5 monitoring data from 55 sites in Shanghai from 2016 to 2019 and the geographic variables, such as road network, points of interest of emission locations, and satellite data were included. We used partial least squares regression (PLS), supervised linear regression (SLR), and random forest (RF) algorithms to develop spatial models and used ordinary kriging (OK) to develop a two-step model. We evaluated the models using a 5-fold cross validation method and selected the best model structure for each modeling approach between one-or two-step models that had been developed with or without OK. The results revealed that the best NO2 models were the RF-OK (Rmse2 was 0.70-0.82) and PLS-OK (Rmse2 was 0.78-0.84) models; the PLS model for PM2.5(Rmse2 was 0.62-0.71) outperformed the other PM2.5 models. We used the best models to predict annual exposures in Shanghai at a 1 km spatial scale and conducted the correlation analysis among the predictions of the best models. The results demonstrated that the NO2 predictions had higher correlation coefficients (r was 0.82-0.91) compared with those of the PM2.5 models (r was 0.66-0.96). Based on the exposure results predicted using the three models in 2019, we evaluated the cumulative population exposure concentrations for NO2 and PM2.5 in Shanghai.

Split injection strategy control map development through prediction-based calibration approach to improve the biodiesel-fuelled diesel engine characteristics.

Karanja oil blended with diesel acts as a good alternative for a CI engine due to its low emission and high oxygen content as compared to pure diesel which leads to the non-toxic and biodegradable nature of the fuel. The objective of this project is to enhance the performance and emission characteristics of a diesel engine that runs on biofuel by accurately calibrating its fuel injector control parameters. To achieve this goal, the project focuses on creating optimal split injector control maps using a novel global model-based calibration approach that considers the entire range of engine speed-load conditions. To develop the model, the experimentation was conducted using the I-optimal design of the experiment technique. To establish a relationship between the calibration parameters and engine performance parameters based on experimental data, the study employed response surface methodology (RSM). With the support of a developed model and multi-objective optimization approach under equivalent importance to performance and emissions, the optimum injector control points are derived. For the developed engine map for 20% KBD (Karanja bio-diesel)-blended fuel, the BTE (brake thermal efficiency) reaches up to 30% and lower BSFC (brake specific fuel consumption) of 0.37 kg/kW-hr. After optimization, the split injector control map showed significant improvements over the un-optimized map. At 19 Nm and 3000 rpm, the optimized map resulted in a 31.36% increase in BTE and a 29.31% decrease in BSFC. Moreover, the optimization successfully balanced the trade-off between reducing nitrogen oxide (NOx) emissions and smoke emissions. However, the optimized fuel map for 20% KBD-blended fuel shows slightly lower performance compared to diesel fuel. While the optimization process led to a decrease in smoke emissions about 22.3%, it also resulted in elevated NOx emissions about 9.83% when compared to diesel fuel. Furthermore, emissions of CO and HC are reduced by 12.8% and 19.2%, respectively, in an optimized control map of 20% KBD-blended fuel compared to un-optimized map.

Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System

This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %.

A Reduced Order Modeling Approach to Capture Transport within a Three-Electrode System Towards Fast Charge Applications

In support of GM's traction battery efforts, we derive and implement a reduced order modeling method to describe the electrochemical performance of a battery cell through the combination of a modified Newman Pseudo-2-Dimensional model and a three-electrode experimental apparatus. To assess the capability of the method, we compare the modeling results with experimental data for a lithiated graphite electrode and a lithiated nickel rich electrode system. The model is applied to simulate the electrochemical and transport processes within the battery cell to predict the negative electrode potential and positive electrode potential with respect to a lithium iron phosphate reference electrode, as well as the terminal voltage.The manufacture and set up of the three electrode system, model development and reduction approaches, as well as a discussion of applicability to fast charge considerations to avoid lithium plating at the anode during a charging event will be presented.

Systematic evaluation of a spatially explicit ecosystem model to inform area-based management in the deep-sea

The long-term provision of ecosystem goods and services depends on the operationalisation of ecosystem-based management approaches that ensure effective conservation and sustainable use of marine resources. This management challenge is addressed internationally through two United Nations instruments: Sustainable Development Goal (SDG) 14 - Life Below Water, and the Convention on the Law of the Sea (UNCLOS) for the Conservation and Sustainable Use of Marine Biological Diversity in Areas Beyond National Jurisdiction (BBNJ Agreement). To achieve sustainability and conservation goals as described in SDG 14 and the BBNJ Agreement, a combination of management tools, including area-based management tools (ABMTs), is necessary. Spatially explicit ecosystem models can inform policy frameworks by enabling ecosystem-wide assessments of ABMTs with indicators that track their management performance. However, the operational use of these complex models depends on the confidence and uncertainty of their predictions. Here, we present a framework to systematically evaluate the performance of a spatially explicit ecosystem model for deep-sea and open-ocean environments, using the ecosystem model for the EEZ of the Azores (NE Atlantic, Portugal) as a case study. The systematic approach aimed to determine the model's suitability as a tool to inform area-based management in the deep-sea. The framework was applied to Ecospace, the spatial-temporal module of the ecological modelling suite Ecopath with Ecosim. It consisted of a stepwise approach for model development and assessment through key parameterisation steps. The steps served for calibration of model parameter values and formal evaluation of temporal and spatial results against the best available reference data. Overall, this approach proved useful in identifying key model sensitivities and sources of uncertainty that arise when considering spatial variability in trophodynamics in the ecosystem model. Moreover, we concluded the model i) effectively predicted the observed inter-annual variability of benthic fish stocks in response to fisheries, trophic interactions, and environmental factors and ii) showed good and moderate spatial goodness-of-fit in replicating reference spatial distribution patterns of stocks and fishing activities. Despite its strengths, the spatial model has limitations related to uncertainties in model parameterisation and the spatial variability of trophodynamics. The systematic assessments presented in this study provide a framework for future model applications to predict the ecosystem-wide impacts of alternative spatial management measures in the deep-sea.

A Guidebook of Group Guidance Services with Role Play ContainsWelas Asih to Prevent Verbal Bullying

The act of verbal bullying is a feeling harsh, the impacts can be generated such are problem of social, psychological and academic problems, from those impacts it is necessary to have a comprehensive prevention effort. This research and development seek to construct a group counselling guideline book using role-play techniques with welas asih to prevent verbal bullying in Junior High School (JHS). This study uses the ADDIE model research and development approach (Analysis, Design, Development, Implementation and Evaluation). Based on the results of the analysis of research data, validation of guidance and counselling material experts, validation of cultural experts, validation of learning media experts, and potential product users as well as effectiveness tests, it can be concluded that the group counselling guideline book use role play technique with welas asih to prevent verbal bullying in Junior High School (JHS) is in the very good category according to the aspects of product acceptability (accuracy, usability, convenience, and attractiveness). Based on the results of limited field tests, it shows that a group counselling guideline book using role-play techniques with welas asih is effective in preventing acts of verbal bullying in Junior High School (JHS).

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Abstract. Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach.

Development of a Multiphase Numerical Modeling Approach for Hydromechanical Behavior of Clay Embankments Subject to Weather-Driven Deterioration

Clay embankments used for road, rail, and flood defense infrastructure experience a suite of weather-driven deterioration processes that lead to a progressive loss of hydromechanical performance: micro-scale deformation (e.g., aggregation and desiccation), changes in soil-water retention, loss of strength, and macro-scale deformation. The objective of this study was to develop a numerical modeling approach to simulate the construction and long-term, weather-driven hydromechanical behavior of clay embankments. Subroutines within a numerical modeling package were developed to capture deterioration processes: (1) strength reduction due to wet-dry cycles; (2) bimodality of the near-surface hydraulic behavior; (3) soil-water and soil-gas retentivity functions considering void ratio dependency; and (4) hydraulic and gas conductivity functions considering void ratio dependency. Uniquely, the modeling approach was comprehensively validated using laboratory tests and nine years of field measurements from a full-scale embankment. The modeling approach captured the variation of near-surface soil moisture and matric suction over the monitored period in response to weather cycles. Further, the developed model approach could successfully simulate weather-driven deterioration processes in clay embankments. The model predictions manifested the ability of the modeling approach in capturing deterioration features such as irrecoverable increases in void ratio and hydraulic permeability near surface. The developed and validated numerical modeling approach enables forecasting the long-term performance of clay embankments under a range of projected climate conditions.

Glass formulation and composition optimization with property models: A review

AbstractGlass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.Graphical abstract

Development of a Swimming Learning Model With a Play Approach to Elementary School Students

The purpose of this study was to develop learning freestyle swimming for elementary school students, as well as to find out the effectiveness of the model that had been developed. The method in this study was to develop a freestyle swimming learning model for elementary school students using the Research & Development model approach from Brog and Gall. The aim of this research is to develop a swimming learning model for students at State Elementary School 1 Lokatabat Utara, State Elementary School Guntung Manggis, State Elementary School Landasan Ulin Utara. The small group trial consisted of 20 people while the large group trial consisted of 110 people. Data collection in this study was to review various literature or literature studies related to the model concepts that would be developed according to the product to be made and referred to needs analysis, expert review and field trials. To calculate the effectiveness test using (t-test) with an analysis of the difference between the two means for dependent samples. The results of the study of the freestyle swimming learning model for elementary school students are in accordance with the characteristics of the students and are effective for improving freestyle swimming learning.

Pengembangan E-Modul IPA Berbasis Pendekatan Saintifik Menggunakan Canva dan Flip PDF Profesional

This research was conducted with the aim of knowing the feasibility of developing science e-modules based on a scientific approach using a combination of the Canva Application and Flip PDF Professional. The research method used is using the ADDIE model research and development (Research & Development/R&D) design and approach. The research instrument used was an e-module validation questionnaire by material experts, media and science learning experts as well as teacher and student practicality questionnaires. The IPA e-module using a combination of Canva and Flip PDF Professional has been successfully developed by integrating aspects of the scientific approach in the e-module learning flow. The expert validation results obtained an average score of 3.57 with a very valid category. The results of the practicality test for teachers and students obtained an average score of 3.75 and 3.89 respectively, so that the e-module practicality category is in the very practical category. Based on the results of the validation and practicality tests, it can be stated that the e-module is feasible and easy to use for science learning purposes for grade VIII students of SMP/MTs.

Prescribed Fire Simulation with Dynamic Ignitions Using Data from UAS-based Sensing

ABSTRACT Prescribed fire is an important tool for wildfire management and land management. Simulation of prescribed fires holds great potential in supporting planning of prescribed burn events. This paper presents a simulation-based study of a prescribed fire using data from Unmanned Aircraft System (UAS)-based sensing. A systematic approach for modelling and simulating prescribed fires with dynamic ignitions is developed. The developed approach is applied to a real prescribed fire where a UAS was used to monitor and collect data about the fire. The dynamic ignition process from multiple fire setting teams is specified, and simulation results are compared to real measurement data from UAS-based sensing. The results demonstrate the effectiveness of the developed modelling approach as well as the utility of using UAS-based fire measurements for prescribed fire simulations.