Learning Modeling Research Articles

Tropical Logistic Regression Model on Space of Phylogenetic Trees

Classification of gene trees is an important task both in the analysis of multi-locus phylogenetic data, and assessment of the convergence of Markov Chain Monte Carlo (MCMC) analyses used in Bayesian phylogenetic tree reconstruction. The logistic regression model is one of the most popular classification models in statistical learning, thanks to its computational speed and interpretability. However, it is not appropriate to directly apply the standard logistic regression model to a set of phylogenetic trees, as the space of phylogenetic trees is non-Euclidean and thus contradicts the standard assumptions on covariates. It is well-known in tropical geometry and phylogenetics that the space of phylogenetic trees is a tropical linear space in terms of the max-plus algebra. Therefore, in this paper, we propose an analogue approach of the logistic regression model in the setting of tropical geometry. Our proposed method outperforms classical logistic regression in terms of Area under the ROC Curve in numerical examples, including with data generated by the multi-species coalescent model. Theoretical properties such as statistical consistency have been proved and generalization error rates have been derived. Finally, our classification algorithm is proposed as an MCMC convergence criterion for Mr Bayes. Unlike the convergence metric used by Mr Bayes which is only dependent on tree topologies, our method is sensitive to branch lengths and therefore provides a more robust metric for convergence. In a test case, it is illustrated that the tropical logistic regression can differentiate between two independently run MCMC chains, even when the standard metric cannot.

Polypropylene waste plastic fiber morphology as an influencing factor on the performance and durability of concrete: Experimental investigation, soft-computing modeling, and economic analysis

The increasing waste of plastic masses raises the danger alarm due to its adverse impacts on people and the natural environment. Hence, serious attempts should be made to manage this non-biodegradable waste for practical, sustainable, and sufficient uses, such as in construction. This research discusses experimentally the possibility of improving the behavior of concrete reinforced with waste plastic fibers (WPFs) from food trays by examining the effect of fibers morphology on concrete's mechanical properties and durability. This concept was intensively studied for steel fibers but rarely for WPFs. A 0.6 percentage of 30 mm long straight, spiral, and channel fibers were tested and compared. Compressive, tensile, and flexural strengths, impact resistance, water absorption, rapid chloride penetration tests, and microstructural analysis were conducted. The results revealed that the fibers shape influences the mechanical behavior along with the water absorption considerably, and that the spiral fibers exhibited superior performance compared to the other shapes. The mechanical properties of spiral fibers specimens have improved considerably compared to specimens of other shapes. However, the ion chloride penetration resistance did not witness any substantial improvement, and the brittle fracture of concrete did not change by using WPFs, despite the considerable increase of more than 66 % of the impact resistance of specimens with spiral fibers. According to the findings, spiral WPFs enhance concrete's behavior, making WPF implementation in concrete more efficient from an environmental and structural standpoint. Moreover, machine learning modeling and cost/benefit analysis were conducted in this work to better understand the WPF effect on concrete.

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data

IntroductionTreatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models...

MODEL PEMBELAJARAN DISCOVERY LEARNING DAN INQUIRY LEARNING PADA PEMBELAJARAN PAI

This article aims to provide a narrative or narrate the learning model of Discovery Learning and Inquiry Learning in PAI learning, this is necessary because PAI learning should not only be carried out based on one-way lectures between educators and students, but students need to take more roles, p. This can be applied if PAI learning uses the Discovery Learning and Inquiry Learning learning models, because by using this model students will be able to find problems, identify them and provide a solution or answer at the final stage. Based on this, we need to know the concept and application of the Discovery Learning and Inquiry Learning models in PAI learning. This research was studied using qualitative research methods using content analysis, namely using books, journal articles and other reading materials relevant to the research. The results of the research are the application of Discovery Learning and Inquiry Learning in PAI learning so that you can dig deeper into the concepts in PAI learning. The conclusion of this research is that Discovery Learning and Inquiry Learning can be applied to learning to deepen concepts and make students more active.

Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning.

Polymer informatics has attracted increasing attention as a specialized branch of material informatics. Hydrophilicity/hydrophobicity is one of the most important properties of interfaces involved in antifouling, self-cleaning, antifogging, oil/water separation, protein adsorption, and bioseparation. Establishing a quantitative structure-property relationship for the hydrophilicity/hydrophobicity of polymeric interfaces could significantly benefit from machine learning modeling. In this study, we aimed to construct machine learning models that could predict the static water contact angle (CA) as an indicator of hydrophilicity/hydrophobicity based on a data set of polymer brushes. The features of the polymer brush surfaces were numerically described using their grafted structures (thickness) and molecular descriptors derived from their chemical structures. We achieved accurate prediction and understanding of important parameters by employing appropriate molecular descriptors considering the Pearson correlation and machine learning models trained with nested cross-validation. The model interpretation by Shapley additive extension analysis indicated that the amount of partial polar/nonpolar structure in the molecule as well as the averaged hydrophobicity represented by MolLogP plays an important role in determining the CA. Moreover, the model can predict the CAs of polymer brushes composed of chemical structures that are not present in existing databases. The CA values of the hypothetical polymer brushes are predicted.

Development of an AI Model Utilizing Buildings’ Thermal Mass to Optimize Heating Energy and Indoor Temperature in a Historical Building Located in a Cold Climate

Historical buildings account for a significant portion of the energy use of today’s building stock, and there are usually limited energy saving measures that can be applied due to antiquarian and esthetic restrictions. The purpose of this case study is to evaluate the use of the building structure of a historical stone building as a heating battery, i.e., to periodically store thermal energy in the building’s structures without physically changing them. The stored heat is later utilized at times of, e.g., high heat demand, to reduce peaking as well as overall heat supply. With the help of Artificial Intelligence and Convolutional Neural Network Deep Learning Modelling, heat supply to the building is controlled by weather forecasting and a binary calendarization of occupancy for the optimization of energy use and power demand under sustained comfortable indoor temperatures. The study performed indicates substantial savings in total (by approximately 30%) and in peaking energy (by approximately 20% based on daily peak powers) in the studied building and suggests that the method can be applied to other, similar cases.

A study of pipeline parallelism in deep neural networks

The current popularity in the application of artificial intelligence to solve complex problems is growing. The appearance of chats based on artificial intelligence or natural language processing has generated the creation of increasingly large and sophisticated neural network models, which are the basis of current developments in artificial intelligence. These neural networks can be composed of billions of parameters and their training is not feasible without the application of approaches based on parallelism. This paper focuses on studying pipeline parallelism, which is one of the most important types of parallelism used to train neural network models in deep learning. In this study we offer a look at the most important concepts related to the topic and we present a detailed analysis of 3 pipeline parallelism libraries: Torchgpipe, FairScale, and DeepSpeed. We analyze important aspects of these libraries such as their implementation and features. In addition, we evaluated them experimentally, carrying out parallel trainings and taking into account aspects such as the number of stages in the training pipeline and the type of balance.

Integration of Various Digital Media with Flipped Classroom Models in Chemistry Learning: An Analysis of Student Activities

This study aims to analyze student activity in flipped learning environments and their response to chemistry learning. The variations of digital media used in this research are YouTube, Miro, Wizer.me, Quizizz, Google Form, and Google Classroom. Participants are 11th grade public high school in Depok city, West Java, Indonesia. The method used is a descriptive qualitative method with data collection techniques through observation sheets, teacher diaries, interviews, reflective journals, and student activity questionnaires. The activity questionnaire in this study used StRIP, adapted from DeMonbrun, et al. Activity indicators consist of two dimensions: student response to instruction and instruction type dimensions. Student activity data is processed using a Likert scale. The results showed that the majority of students responded positively to the second dimension. The results showed that students' responses to instructions and types of instructions developed well during the learning process. This is consistent with the results of the student questionnaire data, which shows a percentage of >70% in each dimension. The affective domain shows that some students have a good attitude of responsibility and discipline. They actively express their opinions and work together in groups. In the psychomotor domain, the students show that they are careful in carrying out and compiling the practicum reports. This study concludes that the flipped classroom learning model using various digital learning media can help develop students' activities in chemistry learning.

An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review

BACKGROUND AND OBJECTIVES: Significant evidence has indicated that the reporting quality of novel predictive models is poor because of confounding by small data sets, inappropriate statistical analyses, and a lack of validation and reproducibility. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement was developed to increase the generalizability of predictive models. This study evaluated the quality of predictive models reported in neurosurgical literature through their compliance with the TRIPOD guidelines. METHODS: Articles reporting prediction models published in the top 5 neurosurgery journals by SCImago Journal Rank-2 (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified through a PubMed search strategy that combined terms related to machine learning and prediction modeling. These original research articles were analyzed against the TRIPOD criteria. RESULTS: A total of 110 articles were assessed with the TRIPOD checklist. The median compliance was 57.4% (IQR: 50.0%-66.7%). Models using machine learning-based models exhibited lower compliance on average compared with conventional learning-based models (57.1%, 50.0%-66.7% vs 68.1%, 50.2%-68.1%, P = .472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures such as confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%). CONCLUSION: Published predictive models in neurosurgery commonly fall short of meeting the established guidelines laid out by TRIPOD for optimal development, validation, and reporting. This lack of compliance may represent the minor extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.

Permafrost Distribution in the Southern Carpathians, Romania, Derived From Machine Learning Modeling

ABSTRACTComputer modeling of sporadic and isolated patches of mountain permafrost distribution is difficult to implement without overestimating it. The main challenge is to determine the very areas where the criteria for permafrost maintenance are met. This paper aims to modeling the permafrost distribution in the Southern Carpathians (SC), a typical marginal periglacial mountain range. For this purpose, a collection of 883 bottom temperature of late winter snow cover (BTS) points was used as a proxy for permafrost presence or absence in order to train several machine learning models. The performances of each model were evaluated with AUC with varying between 0.99 for Maxent and 0.74 for K‐nearest neighbors and most models (five) exhibiting values between 0.82 and 0.86. Other tests such as confusion matrices, sensitivity analyses, data shuffling, and data size reduction tests indicated that Maxent, AdaBoost, and support vector machine offered the best results while logistic regression, neural network, and gradient boosting exhibited rather poor permafrost distributions. The final ensemble median model indicated a total permafrost area of 19.2 km2 occupying 1%–9% of the alpine area of the studied massifs. NDVI proved crucial for permafrost prediction because it allows delimiting the debris surfaces where permafrost is probable.

Ensemble learning soft sensor method of endpoint carbon content and temperature of BOF based on GCN embedding supervised ensemble clustering

Abstract The endpoint control of Basic Oxygen Furnace (BOF) steelmaking depends on the prediction of the endpoint carbon content and temperature. However, predicting these variables is challenging because of the numerous working conditions in the industrial field and the volatility of the sensor data collected during BOF steelmaking. The accuracy of prediction models in ensemble learning depends significantly on the initial distribution of data. However, the complex nature of BOF steelmaking data makes it challenging to generate diverse subsets, which ultimately affects the accuracy of predictions. This paper presents a new approach called Graph Convolutional Network Node Embedding Supervised Ensemble Clustering (GESupEC) for soft sensor modelling in ensemble learning to tackle these issues. GESupEC utilizes a similarity graph derived from a co-association matrix and employs graph convolutional networks to extract structural information among nodes. By optimising the clustering loss within the network, GESupEC learns compact node representations that are useful for the clustering task. Furthermore, it generates a reconstruction matrix based on the similarity of node embeddings. This matrix helps with the extraction of a suitable subset of data for BOF steelmaking through matrix decomposition. After that, the gradient boosting decision tree regression sub-model is established based on the data subset. An ensemble strategy called Gray Relational Analysis Weighted Average is proposed, which assigns weights based on the grey relation similarity between test samples and different data subsets. This weighted average strategy aims to enhance the accuracy of carbon content and temperature predictions. When tested with actual BOF steelmaking generation process data, the prediction accuracy of carbon content reached 88.6% within the error range of ±0.02%, and the prediction accuracy of temperature reached 92.6% within the error range of ±10 °C.

Modelling Anti-Corrosion Coating Performance of Metallic Bipolar Plates for PEM Fuel Cells: A Machine Learning Approach

Proton exchange membrane (PEM) fuel cells have significant potential for clean power generation, yet challenges remain in enhancing their performance, durability, and cost-effectiveness, particularly concerning metallic bipolar plates, which are pivotal for lightweight compact fuel cell stacks. Protective coatings are commonly employed to combat metallic bipolar plate corrosion and enhance water management within stacks. Conventional methods for predicting coating performance in terms of corrosion resistance involve complex physical-electrochemical modelling and extensive experimentation, with significant time and cost. In this study machine learning techniques are employed to model metallic bipolar plate coating performance, diamond-like-carbon coatings of varying thicknesses deposited on SS316L are considered, and coating performance is evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy. The obtained experimental data is split into two datasets for machine learning modelling: one predicting corrosion current density and another predicting impedance parameters. Machine learning models, including extreme gradient boosting (XGB) and artificial neural networks (ANN), are developed, and optimized to predict coating performance attributes. Data preprocessing and hyperparameter tuning are carried out to enhance model accuracy. Results show that ANN outperforms XGB in predicting corrosion current density, achieving an R2 > 0.98, and accurately predicting impedance parameters with an R2 > 0.99, indicating that the models developed are very promising for accurate prediction of the corrosion performance of coated metallic bipolar plates for PEM fuel cells.

Optimizing healthcare resource allocation through data-driven demographic and psychographic analysis

This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, and geospatial data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics and machine learning unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Challenges such as data quality, bias, and ethical considerations underscore the complexity of healthcare resource allocation. However, emerging innovations, including artificial intelligence, precision medicine, and value-based care models, offer transformative potential for optimizing resource allocation and improving patient outcomes. Looking ahead, the integration of innovative technologies and patient-centered care models promises to reshape healthcare resource allocation, emphasizing equity, efficiency, and patient engagement. By navigating challenges thoughtfully and embracing future opportunities, healthcare systems can enhance resource allocation strategies to better meet the evolving needs of patients and communities. This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, administrative databases, geospatial data, and social determinants of health data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics, machine learning, spatial analysis, and simulation modeling unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Keywords: Healthcare Resource Allocation, Demographic Analysis, Psychographic Analysis, Data Sources, Advanced Analysis Techniques, Future Directions.

Universal Design for Learning (UDL) in the Islamic Religious Education Assessment Model

Current curriculum developments place greater emphasis on developing learning models and methods that can accommodate students' differences in learning styles and abilities. In the independent curriculum, this concept is better known as differentiated learning. One concept that can facilitate differentiated learning in the context of assessment is Universal Design for Learning (UDL). Using a Systematic Literature Review (SLR) approach, this research aims to analyze the effectiveness of implementing assessment models in PAI learning in the UDL context. The findings of this research indicate that the assessment model that is widely applied to PAI learning is the authentic assessment model. Based on educational level, this authentic assessment model is more widely applied at the junior secondary education level (SMP/MTs). Based on the results of the analysis of articles that met the inclusion criteria, no evidence was found regarding the effectiveness of its implementation which had an impact on PAI learning processes and outcomes. The findings of this research can be used as an empirical basis for conducting research that focuses on the influence of implementing assessment models on PAI learning processes and outcomes.

Coarse-graining Hamiltonian systems using WSINDy

Weak form equation learning and surrogate modeling has proven to be computationally efficient and robust to measurement noise in a wide range of applications including ODE, PDE, and SDE discovery, as well as in coarse-graining applications, such as homogenization and mean-field descriptions of interacting particle systems. In this work we extend this coarse-graining capability to the setting of Hamiltonian dynamics which possess approximate symmetries associated with timescale separation. A smooth ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon$$\\end{document}-dependent Hamiltonian vector field Xε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$X_\\varepsilon$$\\end{document} possesses an approximate symmetry if the limiting vector field X0=limε→0Xε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$X_0=\\lim _{\\varepsilon \\rightarrow 0}X_\\varepsilon$$\\end{document} possesses an exact symmetry. Such approximate symmetries often lead to the existence of a Hamiltonian system of reduced dimension that may be used to efficiently capture the dynamics of the symmetry-invariant dependent variables. Deriving such reduced systems, or approximating them numerically, is an ongoing challenge. We demonstrate that WSINDy can successfully identify this reduced Hamiltonian system in the presence of large perturbations imparted in the ε>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon >0$$\\end{document} regime, while remaining robust to extrinsic noise. This is significant in part due to the nontrivial means by which such systems are derived analytically. WSINDy naturally preserves the Hamiltonian structure by restricting to a trial basis of Hamiltonian vector fields. The methodology is computationally efficient, often requiring only a single trajectory to learn the global reduced Hamiltonian, and avoiding forward solves in the learning process. In this way, we argue that weak-form equation learning is particularly well-suited for Hamiltonian coarse-graining. Using nearly-periodic Hamiltonian systems as a prototypical class of systems with approximate symmetries, we show that WSINDy robustly identifies the correct leading-order system, with dimension reduced by at least two, upon observation of the relevant degrees of freedom. While our main contribution is computational, we also provide a contribution to the literature on averaging theory by proving that first-order averaging at the level of vector fields preserves Hamiltonian structure in nearly-periodic Hamiltonian systems. This provides theoretical justification for our approach as WSINDy’s computations occur at the level of Hamiltonian vector fields. We illustrate the efficacy of our proposed method using physically relevant examples, including coupled oscillator dynamics, the Hénon–Heiles system for stellar motion within a galaxy, and the dynamics of charged particles.

The impacts of economy policy uncertainty on peer effects of firms R&D investment: Based on LDA machine learning and regression statistical modeling approach.

The Latent Dirichlet Allocation (LDA) model is used to extract the text themes of newspaper news and construct the Chinese Economic Policy Uncertainty (EPU) Index. On this basis, based on the relevant data of Chinese A-share listed companies from 2008 to 2020, this paper empirically analyzes the impact of EPU on peer effects of firms R&D investment, and finds that EPU will aggravate the peer effects of firms R&D investment. Furthermore, the moderating effect of manager's motivation to maintain reputation on the process of EPU influencing the peer effects of firms R&D investment was tested, and the mechanism of EPU influencing the peer effects of firms R&D investment through financial frictions was verified.

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing complex concentrated alloys with quantum machine learning and language modeling

Guru Sebagai Role Model Pribadi yang Baik dalam Pendidikan dan Pembelajaran di Kelas

This article aims to find out teachers as role models in education and learning in the classroom. This research approach is a qualitative approach with the method used in this article literature study. This research is a type of research that collects data from literature. Primary data was obtained from reading reference books, journals, and websites. Data analysis with the stages of data analysis of the Miles and Huberman model with stages of data reduction, data presentation, and verification or conclusion. The validity test of data in qualitative research includes internal validity, external validity, reliability, and objectivity tests. The findings of this research are that for a teacher to be able to carry out the mandate given to him; he must always get closer to Allah SWT. Being a teacher is a hard job, but in this way the position of a teacher becomes noble. For a teacher to be able to carry out his role well, they must be able to be role models for their students. Teachers must be good individuals who are always the Qur'an and Hadith as a guide in doing and behaving. Some of the behaviors that must be possessed by teachers to become a role for their students include teachers must have strong faith, teachers must have good work quality, and teachers must be useful to others.