Developed Model Research Articles

A novel robust MPC scheme established on LMI formulation for surge instability of uncertain compressor system with actuator constraint and piping acoustic

This paper presents a new control method for surge instability of the compressor system. Due to the importance of the active control approach in improving the efficiency of the compressor system, a new scheme based on the model predictive control (MPC) technique has been considered to control surge which gives the benefits of control signal optimization by considering the constraints on states and input. Because of designing a novel MPC by using of linear matrix inequality scheme, the optimization problem is solved in less time and with less complexity. The proposed method is able to consider the limitation of the close coupled valve actuator and also covers the uncertainty on the model. In addition, the developed model describes the nonlinear behaviour of the compressor system and handles the effects of the pipe on surge instability. Using Lyapunov theory, the stability of the closed-loop system under the proposed robust active controller is shown. The simulation results show the high pressure and high efficiency operation of the system under study in different operating conditions.

Application of an offline grey box method for predicting the manoeuvring performance

The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship.

Beyond Traditional Methods: Utilizing a Hybrid SIRe Model for Optimal Counselling Skill Development in Practicum Courses

Beyond Traditional Methods: Utilizing a Hybrid SIRe Model for Optimal Counselling Skill Development in Practicum Courses

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.

Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study.

The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer. The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism. The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% ( P = 0.027); stage II, 72% ( P = 0.014); stage III, 50% ( P = 0.032); and stage IV, 45% ( P = 0.041). The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.

Strategie kulturowe i wspólnotowe wobec ekstraktywistycznego przemysłu krewetkowego na wyspie Puná w Ekwadorze

This article discusses the conflicts created by the expansion of the shrimp industry on the island of Puná in Ecuador and its consequences: the destruction of hundreds of thousands of hectares of mangroves and the landlessness of the island’s communities. The purpose of the text is to show how the principle of community in this particular context becomes a strategy for defending the territory, and thus an alternative to the island’s current extractivist, capitalist development model. Key-words: Puná Island, shrimp industry, extractivism, Ecuadorian communities, mangroves

A framework for the incorporation of digital scholarship contents in South African Library and Information Science Schools

Education curricula need constant updates in response to job market requirements, which may be influenced by the changing technological environment. In the library and information science (LIS) job market there are dynamics brought about by the Fourth Industrial Revolution (4IR). These dynamics call for LIS schools to reconsider their curricula in line with the job market. The job markets expect the education system to reflect their demands, as they are the primary clients. This study explored the education and training of LIS professionals on digital scholarship in line with the LIS curriculum in higher education institutions of South Africa. This was to enable the development of a framework for incorporating digital scholarship into the LIS curriculum. Wolf’s (2007) curriculum development model and an aspect of the customer satisfaction model (Zeithaml et al., 1993) informed the study. The models assisted in producing a framework that can be used to incorporate digital scholarship content into the LIS curriculum in South Africa. The framework suggests that when developing digital scholarship, consultation with relevant stakeholders both internal and external should be considered. Relevant infrastructure and resources should also be in place when developing a digital scholarship curriculum. The relevant infrastructure should include the 4IR-driven infrastructure such as smart sensors, three-dimension (3D) modeling, and cybersecurity tools, among others. Relevant documentation such as resources in the form of policies, course outlines, and other curriculum-related documents should also be consulted. This model, if adopted, could assist LIS schools in developing a curriculum that is driven by digital scholarship, leading to the production of LIS graduates that meet the requirements of the current LIS job market.

An Ecofeminist Contribution to the Debates on the Neoextractivist Development Model in Latin America

This article presents a materialist ecofeminist critique of neoextractivism by highlighting its historical origins and elaborating its economic policy implications in Latin America. Three questions addressed are as follows: 1) How can materialist ecofeminism contribute to understanding the current dynamics of capitalist development in the Global South, 2) why (neo)extractivism hits women hardest, and 3) to what extent and how ecofeminist movements can shape a post-extractivist transition to a just and sustainable future. The article’s main argument is that exploitation and oppression in Latin America can be understood in terms of gender, race, and class and, therefore, require an intersectional analysis framework. Within this framework, post-extractivist alternatives in this region must incorporate an ecofeminist analysis to understand better how social expression systems (including sexism, white supremacy, and ecological crises) intersect and reinforce each other. In this framework, this study is intended to contribute to the growing literature and debate on the development and resistance dynamics of neoextractivism in Latin America, where long-standing racial and gender inequalities intersect with class inequalities.

Shear modulus of sand-rubber mixtures: element testing and constitutive modeling

In this study, a series of resonant column tests was conducted to measure the shear modulus of sand-rubber mixtures at small strain amplitudes (i.e. between 10−4% and 10−2%), considering different rubber percentages and confining stress levels. The results were then combined with data obtained by dynamic hollow cylinder tests to investigate shear modulus degradation of the mixtures over a wider shear strain range. Based on the test results, a new expression was proposed to improve the prediction of maximum shear modulus of sand-rubber mixtures using the modified equivalent void ratio concept. A new constitutive model was also developed for estimation of strain-dependent shear modulus of the mixtures based on the modified hyperbolic framework. The shear modulus of the mixtures was found to be a function of rubber percentage, confining stress, the modified equivalent void ratio and the relative shear stiffness of rubber and sand. The experimental data and the developed models showed that the shear modulus decreased with rubber percentage and increased with confining stress. Moreover, the reference shear strain of the modified hyperbolic model increased with both rubber percentage and confining stress while its curvature coefficient increased more considerably with rubber percentage compared to the confining stress.

Potensi Hutan Mangrove Sebagai Daya Tarik Wisata (Studi Kasus Pada Hutan Mangrove Idaman Kec. Tarowang, Kab. Jeneponto, Prov. Sulawesi Selatan)

The Research of the potential of mangrove forests as an educational tourism is a survey research that focuses on the richness of mangrove forest ecosystems and its potential as an economic driver for the community through educational tourism activities. This research was conducted in the Idaman mangrove forest, Tarowang sub-district, Jeneponto South Sulawesi province. Data collection techniques used were through questionnaires, interviews, and documentation. The results showed that the characteristics of the Idaman mangrove forest consist of various species of mangrove plants, birds, and other mangrove ecosystems that are very potential as a tourist attraction. The potential of the mangrove forest has not been optimized by the communities around tourism area so that it has not yet optimally contributed as an economic source. The constraints in developing of the Idaman mangrove forest as a tourist attraction are the absence of a community-based tourism development and management model on the attractiveness of the Idaman mangrove forest, the low creativity and capacity of the community, as well as limited tourism support facilities. The recommended model for developing mangrove forests is educational tourism based through nursery activities and planting of mangrove forests for conservation, utilization and diversification of mangrove forest products as culinary and handicraft products.

Strategies and Models for Development of Hot Water Tourism Objects in Tombasian Village Under Kawangkoan Barat District, Minahasa Regency

Tourism development is sought to be developed in the context of economic improvement, sustainable development and technological development. Geographically, each region has different characteristics with their own uniqueness and advantages. Natural resources that are owned with all its contents, land in all its forms and the sea with all its contents. This potential can be utilized optimally through tourism. Natural resources owned with all their contents and existing potential will be utilized for development purposes. That potential can be an asset through the tourism sector. One of the attractions that attracts the attention of both domestic and foreign tourists is the hot springs in Tombasian Bawah Village. Tombasian Bawah Village is one of the villages in Kawangkoan Barat District, Minahasa Regency, where in general the people work as farmers and craftsmen on stilt houses. Furthermore, the data that has been collected is analyzed by descriptive analysis using the SWOT analysis method and breaking it down internally and externally. The stages of the research were field observations by collecting data at the physical location, conducting interviews with the government of tourist attraction locations with village programs and district governments related to the development of tourism objects, specifically hot water in the village of Tombasian Bawah and visitors to obtain data. The results of the study show that the Hot Spring Tourism Object Development Strategy is adequate which can be applied by developing tourism on holidays and other holidays. And the model is community based. The development of this hot spring tourism object can be done by diversifying tourism objects and modifying the system so that tourists can travel easily and pleasantly. The results of this research are the proceding/Sentrinov output targets and reports.

A dynamic hybrid model for efficient tuberculosis incidence rate prediction

ABSTRACT Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large.

Time in range prediction using the experimental mobile application in type 1 diabetes

BACKGROUND: Time in range (TIR) is a promising indicator of glycemic control used for evaluation of continuous glucose monitoring (CGM) for patients with diabetes mellitus (DM). The current problem is the assessment and prediction of TIR for patients who use self-monitoring of blood glucose (SМBG) corresponding low CGM availability for the majority of diabetic patients.AIM: To develop a predictive model of TIR for patients with T1DM based on data of the experimental mobile application.MATERIALS AND METHODS: An analysis of 1253 professional CGM profiles of patients with T1DM was performed. On the base of included records, TIR(CGM) was calculated and training models of 7-point SMBG profiles were generated. SMBG profiles’re loaded into the developed experimental mobile application that calculated standard glycemic control parameters. The dataset was divided into main and test samples (80 and 20%). For the main sample, the following methods’re used to develop predictive models: simple linear regression (SLR), multiple linear regression (MLR), artificial neural network (ANN). The effectiveness of the developed models was assessed on the test sample with the calculation of the mean absolute error (MAE), the root mean square error (RMSE).RESULTS: The 568 CGM profiles’re included in the study. TIR in the main group (n=454) — 45 [33; 65]%, in the test group (n=114) — 43 [33; 58]%. The most significant predictors of the regression models were the derived TIR (dTIR), p<0,001; derived time below range level 1 (dTBR1), p<0,001; standard deviation of blood glucose (SD), p=0,007. Determination coefficient for SLR (predictor: dTIR) — 0,844; for MLR (predictors: dTIR, dTBR1, SD) — 0,907. ANN multilayer perceptron models with two and one hidden layers’re developed, with the RMSE on the validation set 4,617 and 6,639%, respectively. The results of the forecast efficiency on the test sample were: dTIR: MAE — 6,82%, RMSE — 8,60%; SLR: MAE — 5,66%, RMSE — 7,34%; MLR: MAE — 4,18%, RMSE — 5,28%; ANN (2 layers): MAE — 4,14%, RMSE — 5,19%; ANN (1 layer): MAE — 4,44%, RMSE — 5,52%.CONCLUSION: ANN with two hidden layers and MLR demonstrated the best ability for TIR prediction. Further studies are required for clinical validation of developed prognostic models.

Extending Finch-Skea isotropic model to anisotropic domain in modified f(, T ) gravity

This paper considers the Finch-Skea isotropic solution and extends its domain to three different anisotropic interiors by using the gravitational decoupling strategy in the context of f(R,T) gravitational theory. For this, we consider that a static spherical spacetime is initially coupled with the perfect matter distribution. We then introduce a Lagrangian corresponding to a new gravitating source by keeping in mind that this new source produces the effect of pressure anisotropy in the parent fluid source. After calculating the field equations for the total matter setup, we apply a transformation on the radial component, ultimately providing two different systems of equations. These two sets are solved independently through different constraints that lead to some new solutions. Further, we consider an exterior spacetime to calculate three constants engaged in the seed Finch-Skea solution at the spherical interface. The estimated radius and mass of a star candidate LMC X-4 are utilized to perform the graphical analysis of the developed models. It is concluded that only the first two resulting models are physically relevant in this modified theory for all the considered parametric choices.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.

AeoLiS: Numerical modelling of coastal dunes and aeolian landform development for real-world applications

The formation and evolution of coastal dunes result from a complex interplay of eco-morphodynamic processes. State-of-the-art models can simulate aeolian transports and morphological dune evolution under certain conditions. However, a model combining these processes for coastal engineering applications was not yet available. This study aims to develop a predictive tool for dune development to inform coastal management decisions and interventions. The aeolian sediment transport model AeoLiS is extended with functionalities that allow for simulations of coastal landforms. The added functionalities include the effect of topographic steering on wind shear, avalanching of steep slopes and vegetation processes in the form of growth and wind shear reduction. The model is validated by simulating four distinct coastal landforms; barchan-, parabolic-, embryo dunes and blowouts. Simulations, based on real-world conditions, replicate the landform formation, migration rates and seasonal variability.

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy.

While large-scale artificial intelligence (AI) models for protein structure prediction and design are advancing rapidly, the translation of deep learning models for practical macromolecular drug development remains limited. This investigation aims to bridge this gap by combining cutting-edge methodologies to create a novel peptide-based PROTAC drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified binding peptides for androgen receptor (AR) and Von Hippel-Lindau (VHL), followed by computational modeling with Alphafold2-multimer and ZDOCK to predict spatial interrelationships. Experimental validation confirmed the designed peptide's binding ability to AR and VHL. Transdermal microneedle patching technology was seamlessly integrated for the peptide PROTAC drug delivery in androgenic alopecia treatment. In summary, our approach provides a generic method for generating peptide PROTACs and offers a practical application for designing potential therapeutic drugs for androgenetic alopecia. This showcases the potential of interdisciplinary approaches in advancing drug development and personalized medicine.

Modeling of Texture Development during Metal Forming Using Finite Element Visco-Plastic Self-Consistent Model

In directional forming processes, such as rolling and extrusion, the grains can develop preferred crystal orientations. These preferred orientations—the texture—are the main cause for material anisotropy. This anisotropy leads to phenomena such as earing, which occur during further forming processes, e.g., during the deep drawing of sheet metal. Considering anisotropic properties in numerical simulations allows us to investigate the effects of texture-dependent defects in forming processes and the development of possible solutions. Purely phenomenological models for modeling anisotropy work by fitting material parameters or applying measured anisotropy properties to all elements of the part, which remain constant over the duration of the simulation. In contrast, crystal plasticity methods, such as the visco-plastic self-consistent (VPSC) model, provide a deeper insight into the development of the material microstructure. By experimentally measuring the initial texture and using it as an initial condition for the simulations, it is possible to predict the evolution of the microstructure and the resulting effect on the mechanical properties during forming operations. The results of the simulations with the VPSC model show a good agreement with corresponding compression tests and the earing phenomenon, which is typical for cup deep drawing.

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Developing Teachers’ Integrated STEM Education Knowledge and Lesson Planning Skills: A Professional Development Model for Preschool and Primary School Teachers

The effective implementation of STEM education relies on qualified and well-educated teachers who employ good practices for integrated STEM education. Quality STEM education equips children with the necessary skills to address 21st-century challenges, playing a critical role in developing critical thinking, communication, collaboration, creativity, problem-solving, and responsibility. Preschool and primary school teachers were recommended to attend STEM Professional Development (PD). This study aimed to determine the impact of the EarlySTEM Leader Teacher PD Program on teachers' ability to design STEM lesson plans. Researchers analyzed the STEM lesson plans prepared by preschool and primary school teachers (n=33) who participated in the integrated STEM PD program. The plans were evaluated using the Authentic Problems of Knowledge Society (APoKS) rubric. The findings showed that the STEM PD program positively influenced teachers' lesson planning skills, specifically when writing the APoKS. The implications of the study were discussed in detail. Keywords: EarlySTEM education, teachers’ professional development, early childhood, primary school, preschool