Developed Model Research Articles

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

User interface design of context-input-process-product evaluation application based on weighted product

This study aimed to show the user interface design form of the <br /> context-input-process-product (CIPP) evaluation application based on weighted product as a measuring tool for the effectiveness level of blended learning in health colleges. This research approach was development research. The development model used was Borg and Gall. It focused on the design stage, initial trials, and revisions. The initial test of the user interface design involved 32 respondents. The tool for conducting it was in the form of a questionnaire, which contains 16 questions. The research was at the health colleges in Buleleng Regency. The data analysis technique of the initial test results was quantitative descriptive. It compared the percentage level of user interface design quality from the weighted product-based CIPP evaluation application with a quality standard which referred to a five scale. The results of this study indicated that the quality of the user interface design was relatively good. The research result’s impact on educational evaluation was new knowledge for pedagogic evaluators in maximizing the development of digital-based evaluation tools by integrating the decision support system method (weighted product) with the educational evaluation model (CIPP model).

Performance analysis of different control models for smart demand–supply energy management system

Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

Addressing the Water-Energy-Food Nexus in the context of climate change: the case of Bagré dam in Burkina Faso

The water-energy-food (WEF) nexus concept highlights the interdependencies between water, energy, and food resources. Assessing these interdependencies can lead to a better understanding and their exploitation to improve people's access to water, energy, and food. This study aims to highlight the links between the water resource and the current and future water requirements for agricultural and hydropower production in the context of climate change. The bagré dam which is a multi-usage reservoir located in Burkina Faso is considered as the case study. This dam is used for agricultural production, livestock, and hydroelectricity production. To achieve this objective, the surface water resources of the study area and the different water uses have been first estimaded. Then, a model has been developed and implemented using the Water Evaluation And Planning (WEAP) tool, with the reference year 2020. Finally, the developed model was used to study four relevant plausible scenarios in the Bagré area up to 2050, namely: (1) expansion of irrigated schemes, (2) the drinking water needs of a neighbouring town, (3) climate change effects on the dam's water capacity, and (4) combinaison of the three other scenarios. The results show that all the water requirements for different uses are fully met for all the scenario except the combined scenario by 2050. For this last scenario, the unmet water demands reach up to 100% specially during dry season. So, to improve the coverage rate of water demands for the combined scenario, it is crucial to explore other alternatives such as the use of groundwater and the implementation of best practices in agronomic management.

A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials

Accurate and stable prediction of the effective thermal conductivity (ETC) of porous ceramic materials is of great significance for their application in areas such as optimizing the design of thermal barrier coatings and improving energy conversion efficiency. Porous ceramic materials exhibit complex porous structures with gradient porosity distributions that pose a great challenge for effective medium theory (EMT) and conventional convolutional neural networks (CNN) in attempting to accurately and stably predict the ETC of porous media. In this study, a CNN model that integrates self-attention and a multiscale feature-fusion mechanism is proposed to predict the ETC of porous media with greater accuracy and stability. The integration of the self-attention and multiscale feature-fusion mechanisms enhances the CNN's ability to learn long-range dependencies and preserve detailed information. The optimization of the CNN's accuracy and stability is visually illustrated using gradient-weighted class activation mapping (Grad-CAM) for ETC. Additionally, by employing the proposed gradient quartet structure generation set (QSGS), a gradient porous ceramic media dataset comprising 10 000 images was built to train the CNN model. Finally, the prediction results and relative error distribution of the ETC were compared across different models. Our model demonstrated improvements in statistical metrics, including a 33.7 % decrease in mean error, 25.2 % decrease in median error, and 59.6 % decrease in maximum error. The decreases in these metrics and Grad-CAM for the ETC strongly demonstrates that the model proposed in this work greatly improved the accuracy and stability of predicting the ETC of ceramic materials with gradient porosity distributions.

An Exploration of Organoid Technology: Present Advancements, Applications, and Obstacles.

Organoids are in vitro models that exhibit a three-dimensional structure and effectively replicate the structural and physiological features of human organs. This work examines the potential applications of organoid technology, as well as the challenges and future directions associated with its implementation. The manuscript was put together by conducting a comprehensive literature review, which involved an in-depth evaluation of globally renowned scientific research databases. The field of organoids has generated significant attention due to its potential applications in tissue development and disease modelling, as well as its implications for personalised medicine, drug screening, and cell-based therapies. The utilisation of organoids has proven to be effective in the examination of various conditions, encompassing genetic disorders, cancer, neurodevelopmental disorders and infectious diseases. The exploration of the wider uses of organoids is still in its early phases. Research shall be conducted to integrate 3D organoid systems as alternatives for current models, potentially improving both fundamental and clinical studies in the future.

New control scheme for a dynamic voltage restorer based on selective harmonic injection technique with repetitive controller

Repetitive controller and selective harmonic injection technique (SHI) in medium and low voltage distribution networks improve dynamic voltage restorer (DVR) DC bus voltages as well as nullify power quality (PQ) problems. DVRs use sinusoidal pulse width modulation (SPWM) firing control, but DC bus use seems to be limited, affecting density, cost, and power packaging. By adding 1/6th of the 3rd harmonic waveform to the basic waveform, SPWM yields the developed model. According to the findings, 15% of DC bus usage improves and produces high voltage AC. Nevertheless, just control systems perturb PQ. The proposed controller uses feed forward and feedback to enhance transient response and justify stable zero error. 3rd third harmonic injection pulse width modulation (THIPWM) improves total harmonic distortion (THD) in the proposed scheme. Power system computer aided design (PSCAD) simulation produced high accuracy for THIPWM and repetitive controllers.

Resource forecasting under the action of degradation processes with a catastrophic section on the example of universal spindles liners of rolling mills

The problem of forecasting the beginning of the repair effects of the sheet-rolling equipment has been examined using the example of universal spindle liners. Executive intervention in the case of critical wear of the liners allows you to avoid emergency situations associated with the failure of the universal spindle, and therefore increases the productivity of the rolling mill and its profitability. Method of setting the replacement date of liners, which for the considered case is on the 29th day of their operation, has been proposed. The operation of liners in the zone of emergency wear has been studied, the developed model determines that when working in the zone of catastrophic wear, the use of liners is possible for no more than 20 hours. The task of forecasting the resource under the influence of degradation processes with a catastrophic section has been solved. The determination of the technical condition in this case is carried out on the basis of parametric models, where the rate of change of the diagnostic parameter is controlled. The critical value of the speed is determined on the basis of a constant trend, which is multiplied by the number of the reserve, equal to nV = 8–10.

Models and policies for family development, population control, and family planning: a scoping review

Uncontrolled population growth causes poverty, hunger, disease and death. Ironically, population explosions usually occur in developing countries, while population growth is usually low, zero, or even negative in developed countries. Considering these factors, this research aims to formulate effective family planning and population control methods to improve the quality of family development. In this research we present a scoping review article regarding family development models and policies, population control, and family planning. A comprehensive search was conducted in major databases, including PubMed, EBSCO, ProQuest, and ScienceDirect, via presentation methods using PRISMA-ScR guidelines. As a result, we found 16 articles meeting the eligibility criteria. The findings of this research provide insight to stakeholders and researchers into how family development, population control, and family planning policies can be modeled so that effective results can be achieved.

Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications.

A penta-linear backbone model for unbonded post-tensioned concrete masonry shear walls

Unbonded post-tensioned concrete masonry (UPCM) shear walls are an effective seismic force resisting system due to their ability to mitigate expected damage through their self-centering capabilities. A few design procedures were proposed to predict the in-plane flexural response of UPCM walls, albeit following only basic mechanics and/or extensive iterative methods. Such procedures, however, may not be capable of capturing the complex nonlinear relationships between different parameters that affect UPCM walls’ behavior or are tedious to be adopted for design practice. In addition, the limited datasets used to validate these procedures may cast doubt on their accuracy and validity for new, unseen data, further hindering their adoption by practitioners and design standards. To address these issues, an experimentally-validated nonlinear numerical model was adopted in this study and subsequently employed to simulate 95 UPCM walls with different design parameters to compensate for the lack of relevant experimental data in the current literature. Guided by mechanics and using this database, an evolutionary algorithm, multigene genetic programming (MGGP), was adopted to uncover the relationships controlling the response of UPCM walls, and subsequently develop simplified closed-form wall behavior prediction expressions. Specifically, through integrating MGGP and basic mechanics, a penta-linear backbone model was developed to predict the load-displacement backbone for UPCM walls up to 20% strength degradation. Compared to existing predictive procedures, the prediction accuracy of the developed model and its closed-form nature are expected to facilitate better understanding of such wall behaviors and subsequently open the gate to further incorporate other experimental and numerical results to ultimately incorporate UPCM in mainstream designs in the near future.

Advanced Ensemble Framework for Diabetes Outcome Forecasting

Abstract: Diabetes is one of the chronic diseases, which is increasing from year to year. Developing an automated system that can detect diabetes patients plays an important role in medical science. A stack classifier model is designed for the detection of diabetes by combining three base estimators such as Random Forest Classifier, LightGBM Classifier, and K-Nearest Neighbors Classifier, and Logistic Regression as meta-classifiers. The data preprocessing includes transforming categorical variables into numerical format. Each base learner is trained on the preprocessed data and predictions are made. In the meta learner stage, Logistic Regression is trained to make predictions based on the predictions of the base learners. The goal of the meta learner is to learn how to combine the predictions of the base learners to make a more accurate final prediction on whether the patient is diabetic or not. The use of a stacking classifier improves prediction accuracy compared to using a single classifier. The developed model gives an accuracy of 98%. The 5-fold cross validation is used to get a more robust estimation of generalization error. Thus, the developed model offers a means to enhance early detection and elevate the quality of care for diabetic patients.

Developing Canva-based E-Module of flat surfaces in geometry for eighth

This development research is aimed at developing an e-module of flat surfaces in geometry for eighth graders and describing it based on validity, practicality, and effectiveness aspects. We used the 4D model, i.e., define, design, develop, and disseminate, as the development model and assessment sheets for the e-module as research instruments, later filled out by material and media experts to measure their validity. Other research instruments included assessment sheets for teachers as practitioners to measure practicality, student response questionnaires, and learning outcome test to measure the e-module’s effectiveness. The product was tested on 30 eighth graders, and results demonstrated that the material expert gave a mean score of 4.2 with a good criterion, and the media expert gave a score of 4.8 with an excellent criterion. Accordingly, the e-module developed was considered valid. Practitioners gave a mean score of 4.8 to the e-module, therefore classified as excellent and practical. Student responses were very positive to the e-module at 91.71%, and the classical completeness percentage based on the student learning test results was 96.67%, thereby declaring that the e-module was effective.

The trophoblast surface becomes refractory to adhesion by congenitally transmitted Toxoplasma gondii and Listeria monocytogenes during cytotrophoblast to syncytiotrophoblast development.

The placenta is a critical barrier against viral, bacterial, and eukaryotic pathogens. For most teratogenic pathogens, the precise molecular mechanisms of placental resistance are still being unraveled. Given the importance of understanding these mechanisms and challenges in replicating trophoblast-pathogen interactions using in vitro models, we tested an existing stem-cell-derived model of trophoblast development for its relevance to infection with Toxoplasma gondii. We grew human trophoblast stem cells (TSCT) under conditions leading to either syncytiotrophoblast (TSSYN) or cytotrophoblast (TSCYT) and infected them with T. gondii. We evaluated T. gondii proliferation and invasion, cell ultrastructure, as well as for transcriptome changes after infection. TSSYNs cells showed similar ultrastructure compared to primary cells and villous explants when analyzed by transmission electron microscopy and scanning electron microscopy (SEM), a resistance to T. gondii adhesion could be visualized on the SEM level. Furthermore, TSSYNs were highly refractory to parasite adhesion and replication, while TSCYTs were not. RNA-seq data on mock-treated and infected cells identified differences between cell types as well as how they responded to T. gondii infection. We also evaluated if TSSC-derived SYNs and CYTs had distinct resistance profiles to another vertically transmitted facultative intracellular pathogen, Listeria monocytogenes. We demonstrate that TSSYNs are highly resistant to L. monocytogenes, while TSCYTs are not. Like T. gondii, TSSYN resistance to L. monocytogenes was at the level of bacterial adhesion. Altogether, our data indicate that stem-cell-derived trophoblasts recapitulate resistance profiles of primary cells to T. gondii and highlight the critical importance of the placental surface in cell-autonomous resistance to teratogens.IMPORTANCECongenital toxoplasmosis can cause a devastating consequence to the fetus. To reach the fetus's tissues, Toxoplasma gondii must cross the placenta barrier. However, how this parasite crosses the placenta and the precise molecular mechanisms of placental resistance to this parasite are still unknown. In this study, we aimed to characterize a new cellular model of human trophoblast stem cells to determine their resistance, susceptibility, and response to T. gondii. Syncytiotrophoblast derived from trophoblast stem cells recapitulate the resistance profile similarly to placenta cells. We also showed that these cells are highly resistant to Listeria monocytogenes, at the level of bacterial adhesion. Our results suggest that resisting pathogen adhesion/attachment may be a generalized mechanism of syncytiotrophoblast resistance, and trophoblast stem cells represent a promising model to investigate cell-intrinsic mechanisms of resistance to pathogen adhesion and replication.

A novel method of refining the performance of rail systems: an evaluation of system dynamics using discrete event simulation

PurposeThis paper aims to trial a novel method of improving the performance of rail systems. Accordingly, an evaluation of rail system dynamics (SD) using discrete event simulation (DES) will be undertaken. Globally, cities and their transportation systems face ongoing challenges with many of these resulting from complicated rail SD. To evaluate these challenges, this study utilized DES as the basis of the analysis of Melbourne Metro Rail's SD. The transportation SD processes including efficiency and reliability were also developed.Design/methodology/approachUsing DES, this research examines and determines the Melbourne Metro Rail's SD. Although the Melbourne Metro Rail is still in progress, the DES developed in this research examined the system requirements of functionality, performance and integration. As the basis of this examination, the Melbourne Metro Rail's optimization was simulated using the developed DES. As the basis of the experiment, a total of 50 trials were simulated. This included 25 samples for each of efficiency and reliability. The simulation not only scrutinized the SD but also underlined some of its shortfalls.FindingsThis study found that information and communication technology (ICT) was the pinnacle of system application. The DES development highlighted that both efficiency and reliability rates are the essential SD and thus fundamental for Melbourne Metro Rail system functionality. Specifically, the three elements of SD, capacity, continuity and integration are considered critical in improving the system functionality of Melbourne Metro Rail.Research limitations/implicationsThis particular mega rail infrastructure system was carefully analyzed, and subsequently, the DES was developed. However, since the DES is at its inception, the results are relatively limited without inclusive system calibration or validation process. Nonetheless, with some modifications, such as using different KPIs to evaluate additional systems variables and setting appropriate parameters to test the system reliability measures at different intensities, the developed DES can be modified to examine and evaluate other rail systems. However, if a broader system analysis is required, the DES model subsequently needs to be modified to specific system parameters.Practical implicationsThrough evaluation of Melbourne's Metro Rail in the manner described above, this research has shown the developed DES is a useful platform to understand and evaluate system efficiency and reliability. Such an evaluation is considered important when implementing new transport systems, particularly when they are being integrated into existing networks.Social implicationsEfficient rail networks are critical for modern cities and such systems, while inherently complex, aid local economies and societal cohesion through predictable and reliable movement of people. Through improved system functionality and greater efficiencies, plus improved passenger safety, security and comfort, the traveling public will benefit from the enhanced reliability of the transportation network that results from research as that provided in this paper.Originality/valueThis research paper is the first of its kind specifically focusing on the application of DES on the Melbourne Metro Rail System. The developed model aligns with the efficiency optimization framework, which is central to rail systems. The model shows the relationship between increased efficiency and optimizing system reliability. In comparison with more advanced mathematical modeling, the DES presented in this research provides robust, but yet rapid and uncomplicated system enhancements. These findings can better prepare rail professionals to adequately plan and devise appropriate system measures.

Development and verification of a full physiologically-based pharmacokinetic model for sublingual buprenorphine in healthy adult volunteers that accounts for nonlinear bioavailability.

Sublingual buprenorphine is used for opioid use disorder and neonatal opioid withdrawal syndrome (NOWS). The study aimed to develop a full physiologically-based pharmacokinetic (PBPK) model that can adequately describe dose- and formulation-dependent bioavailability of buprenorphine. Simcyp (v21.0) was used for model construction. Four linear regression models (i.e. untransformed or log-transformed for dose or proportion sublingually absorbed) were explored to describe sublingual absorption of buprenorphine across dose. Published clinical trial data not used in model development were used for verification. The PBPK model's predictive performance was deemed adequate if the geometric means of ratios between predicted and observed (P/O ratios) area under the curve (AUC), peak concentration (Cmax), and time to reach Cmax (Tmax) fell within the 1.25-fold prediction error range. Sublingual buprenorphine absorption was best described by a regression model with logarithmically transformed dose. By integrating this nonlinear absorption profile, the PBPK model adequately predicted buprenorphine pharmacokinetics (PK) following administration of sublingual tablets and solution across a dose range of 2-32 mg, with geometric mean (95% confidence interval) P/O ratios for AUC and Cmax equaling 0.99 (0.86-1.12) and 1.24 (1.09-1.40), respectively, and median (5th to 95th percentile) for Tmax equaling 1.11 (0.69-1.57). A verified PBPK model was developed that adequately predicts dose- and formulation-dependent buprenorphine PK following sublingual administration. Significance Statement The PBPK model developed in this study is the first to adequately predict dose- and formulation-dependent sublingual buprenorphine pharmacokinetics. Accurate prediction was facilitated by the incorporation of a novel nonlinear absorption model. The developed model will serve as the foundation for fetomaternal PBPK modeling to predict maternal and fetal buprenorphine exposures to optimize buprenorphine treatment for neonatal opioid withdrawal syndrome (NOWS).

Thermodynamic performance investigation of environmentally friendly working fluids in a geothermal integrated pumped thermal energy storage system

Abstract Among the available energy storage technologies, pumped thermal energy storage (PTES) is emerging as a potential solution for large-scale electrical energy storage with no geographical limitations and high roundtrip efficiencies. However, PTES requires a low-cost, high-temperature heat source to achieve reasonable roundtrip efficiencies. Moreover, organic Rankine cycle-based PTES systems require high-performance and environmentally friendly working fluids. In this study, the thermodynamic performance of a geothermal integrated PTES system using environmentally friendly working fluids is investigated. The mathematical model of the geothermal integrated PTES system is developed using the first and second laws of thermodynamics and implemented in Engineering Equation Solver (EES). With the developed model, the thermodynamic performance of the PTES system for different working fluids, including Butene, cyclopentane, iso-butane, R1233zd(E), R1234ze(Z), R1224yd(Z), HFO1336-mzz(Z), R365mfc, n-hexane, and n-pentane was investigated. For geothermal fluid outlet temperatures between 60°C and 120°C and geothermal fluid inlet and outlet temperature differences across the evaporator between 20°C and 60°C, the net power ratio i.e., the ratio of the electrical energy discharged to the electrical energy used to run the charging cycle, is between 0.25 and 1.40. This shows that the system has the potential to give back more than 100% of the electrical energy used during charging under certain conditions. High net power ratios are obtained for a combination of high source temperatures and low geothermal fluid inlet and outlet temperature differences.

An improved model for primary prediction of performance map for turbocharger radial turbine

In the contemporary landscape, possessing an intricate understanding of the performance characteristics of turbocharger radial turbine proves invaluable during engine development phases, to improve predictive capabilities of calculation codes and enhance the critical process of matching engines with turbochargers. This research deals with offering two precise yet straightforward analytical functions intended to generate comprehensive performance maps of turbocharger turbines. This is achieved through a refined adjustment of a preexisting analytical function, after introducing an inventive multiplication factor that aligns numerical calculations with experimental data to predict the turbine’s expansion ratio. Besides, a second analytical function forecasts the turbine’s thermo-mechanical efficiency by establishing a power balance equation between the turbine and supplied compressor map. The outcome of the developed model is compared with existing method on two distinct turbochargers, encompassing various rotational speeds. Additionally, a sensitive analysis aiming to detect the most important factors affecting our developed model while exploring it possible validity range for different thermodynamic parameters. The results indicate that the two functions yield reliable estimations of turbine performance, with maximum; root mean square error, R2, and mean absolute percentage error indices find around 9.47%, 0.993, and 9.03% for the turbine expansion ratio, and about 4.42%, 0.612, and 19.78% for efficiency prediction. This novel model enhances simulation accuracy while preserving user-friendliness and robustness based on the prerequisite of limited geometric and thermodynamic parameters at the turbocharger boundaries. Finally, the main advantages of the proposed model is its adaptability for the implementation in calculation codes, turbomachinery optimization strategies and assessments of the design and performance, addressing scenarios where the original turbine maps are rarely provided by turbocharger manufacturers.

From Concept to Community Impact: the ABCD Model's Trailblazing Role in Community Service Programs

Objective: This study aims to develop a unique model for the Community Service Program (CSP) at UIN Imam Bonjol Padang by emphasising the ABCD (Asset-Based Community Development) approach. Methodology: Involving CSP activities from 2016 to 2023, this study aims to enrich the CSP program by integrating innovative strategies in community empowerment. The research adopted the 4D Model (Define, Design, Develop, Disseminate) for model development, by organising the CSP framework. Using a qualitative approach, the research method involved appreciative inquiry, community mapping, field interviews, and surveys in 10 districts/cities in West Sumatra. Analyses of CSP activities reveal distinctive patterns-Thematic CSP, CSP-PPM, and different types of CSP-that impact community empowerment. Results: The findings emphasise the need for structured, sustainable, and tailored CSP programs, with an emphasis on continuity of planning, program stability, and sustained impact for lasting community benefit. This study presents an ABCD pattern of service-learning, emphasising collaboration, asset building, and problem-solving, contained within the 4D framework. Conclusion: The model highlights structured planning stages, debriefing rules, and comprehensive engagement strategies that are essential for the implementation of impactful service-learning.

Development of die drawing design factor prediction models for trenchless rehabilitation of water pipes: a case study

ABSTRACT Maintenance and replacement of aging water pipes are critical for urban development. To this end, trenchless rehabilitation methods that do not require excavation offer an efficient replacement for aging water pipes. Thus, this study developed a trenchless method using die drawing, which continuously inserts polyethylene (PE) liners into the reach point while maintaining traction in the host aging pipe. If the traction is removed after a certain period, the initially contracted liner pipe undergoes expansion under natural restoration and tightly adheres to the inner wall of the host pipe to achieve structural regeneration of the aging pipe. This study aimed to determine the factors influencing the die drawing process using a rehabilitation design. Accordingly, we experimented with the die drawing method to achieve structural reinforcement using high elasticity PE liners. Thereafter, based on the experimental data, we developed prediction models to estimate traction, pipe restoration rate after traction release, and pipe restoration period. The developed models estimated the minimum required restoration period and provided deeper insights into the restoration behavior of the die drawing method employed in pre-equipment maintenance before on-site construction.