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Modeling and numerical simulation of control policies for co-infection Leishmaniasis–Chagas disease in Brazil via classical and fractional RK4-scheme
(2026-08-01)
Aalam, Balal
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ur-Rehman, Daniyal
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Ghaffar, Maryam
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Pongsumpun, Puntani
Vector-borne diseases have long played a significant role in human mortality and public health crises in Brazil. Among these, neglected tropical diseases such as Leishmaniasis and Chagas disease require urgent attention. This paper develops a deterministic mathematical dynamical model to study the dynamics of mono and co-infection with Leishmaniasis and Chagas disease. We begin with a rigorous mathematical analysis of the model, including the computation of the basic reproduction number R<inf>0</inf>LC and its sensitivity indices, which help identify key parameters driving disease dynamics. The population's equilibrium states are studied in relation to this threshold parameter. Furthermore, we incorporate four control prevention into the model to evaluate the best intervention policy. Using a reliable data set from Brazil over a specified time period, we estimate model parameters and fit the data, demonstrating strong agreement between the model predictions and the actual data. Our graphical simulations further support the findings. Additionally, we compute and analyze the controlled reproduction number, confirming that the proposed policy 5 is the most effective in reducing the disease burden in Brazil. Finally, we rewrite the fractional version of the model and perform numerical simulations for different values of α. The fractional-order model is conceptually suitable for capturing memory effects and delay response in disease transmission dynamics. This study provides valuable insights for public health stakeholders and health care centers aiming to design efficient prevention and control programs for neglected vector-borne diseases.
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Real-time interpretable and cluster-stratified lightGBM framework for high-precision concrete strength prediction and instantaneous mixture optimization
(2026-08-29)
Elsheikh, Ahmed
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Hematibahar, Mohammad
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Jueyendah, Sebghatullah
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Aljarah, Abdelmalek H.
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Martins, Carlos Humberto
This study presents a real-time, interpretable framework based on the light gradient boosting machine (LightGBM) algorithm for the accurate prediction and optimization of 28-day concrete compressive strength (Fc), validated using a dataset of 500 concrete mixtures. The proposed model was benchmarked against seven widely used regression algorithms, including linear regression (LR), ridge regression (RR), random forest (RF), K-nearest neighbors (KNN), support vector regression (SVR), decision tree (DT), and multivariate adaptive regression splines (MARS), to ensure a comprehensive comparative evaluation. The LightGBM model demonstrated superior predictive performance relative to the benchmark models, achieving an RMSE of 6.11 MPa and an R² of 0.951 during the initial evaluation. Model robustness and generalization capability were further verified using a 10 × 10 repeated k-fold cross-validation procedure, yielding stable results (R² = 0.940 ± 0.017; RMSE = 6.37 ± 0.49 MPa). To capture heterogeneity in mixture compositions, K-means clustering was applied to partition the dataset into four distinct mixture regimes, within which stratified LightGBM models further improved predictive accuracy, reducing RMSE to 3.7–5.1 MPa and achieving R² values exceeding 0.97. Model interpretability was enhanced through global and regime-specific SHAP (Shapley Additive Explanations) analyses, which provided transparent and physically consistent insights into feature contributions, consistently identifying cement as the dominant positive factor and water as the primary negative driver of CS. Furthermore, an interactive web-based prediction engine was developed to enable instantaneous strength prediction, real-time sensitivity analysis, 95% prediction interval estimation, and specification-driven mixture optimization with millisecond-level computational efficiency. Comprehensive diagnostic evaluations, including Taylor diagrams, residual control charts, calibration plots, and prediction-interval validation, confirmed the statistical reliability and practical applicability of the proposed framework. Overall, the developed LightGBM-based system provides an accurate, interpretable, and scalable decision-support tool for data-driven concrete mix design and performance optimization.
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Mathematical modeling and optimal control analysis of classical and fractional order SVEITR model for TB infection disease in KPK Province of Pakistan
(2026-08-01)
Aalam, Balal
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Pongsumpun, Puntani
In Pakistan, tuberculosis (TB) is still a significant public health concern. To address the socioeconomic and healthcare issues in the Khyber Pakhtunkhwa (KPK) province, this study offers a novel mathematical model of tuberculosis transmission. To the best of my knowledge, this is the first optimal control study of SVEITR-TB dynamics in KPK, Pakistan, incorporating both classical and fractional-order modeling frameworks to capture memory effects and complex disease behavior. Model validity is ensured through existence and uniqueness analysis, and the basic reproduction number is used to predict future disease dynamics. Model stability is assessed using Routh-Hurwitz criteria, Castillo–Chavez theorem, and Lyapunov functions for disease-free and endemic scenarios. In addition, backward bifurcation analysis is discussed near the bifurcation point. A sensitivity analysis is conducted to identify the key parameters that affect disease spread. The Nonstandard Finite Difference (NSFD) technique is used for numerical simulations of the deterministic model, and the fractional RK2 approach is used to simulate the fractional-order formulation, showing the disease can be controlled over time. The findings show that the fractional RK2 scheme successfully captures the memory effects present in the fractional-order dynamics and improves numerical accuracy. Furthermore, optimal control strategies, including enhanced vaccination and enhanced treatment, are assessed using Pontryagin’s maximum principle. Simulations using the RK4 forward-backward sweep method show that strategy A is the most reliable for controlling TB among the control measures, with a highest cumulative efficiency index. This demonstrates that strategy A is the most suitable control measure, providing the greatest reduction in disease burden. Thus, we conclude that stockholders and policymakers can use strategy A to control TB in the future.
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Modeling the compressive strength behavior of concrete reinforced with basalt fiber
(2025-12-01)
Onyelowe, Kennedy C.
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Ebid, Ahmed M.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Awoyera, Paul
This research investigates the compressive strength behavior of basalt fiber-reinforced concrete (BFRC) using machine learning models to optimize predictions and enhance its practical applications. The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. Basalt Fiber Reinforced Concrete (BFRC) is a composite material that incorporates basalt fibers into traditional concrete to enhance its mechanical and durability properties. The use of basalt fibers, derived from natural volcanic rocks, aligns with sustainability goals due to their eco-friendliness, cost-effectiveness, and high performance. BFRC combines structural excellence with sustainability, making it an ideal material for modern construction practices. Its ability to enhance performance, reduce environmental impact, and ensure long-term durability positions it as a pivotal solution for sustainable infrastructure development. The developed models were used to predict compressive strength of basalt fiber concrete (Cs_bf) using the concrete mixture contents, age, and fiber dimensions. All the developed models were created using “Orange Data Mining” software version 3.36. A total of three hundred and nine (309) records were collected from literature for compressive strength for different mixing ratios of basalt fiber concrete with concrete at different ages. Each record contains the following data: C-Cement content (Kg/m3), FA-Fly ash content (Kg/m3), W-Water content (Kg/m3), SP-Super-plasticizer content (Kg/m3), CAg-Coarse aggregates content (Kg/m3), FAg-Fine aggregates content (Kg/m3), Age-The concrete age at testing (days), L_b-length of basalt fibers (mm), d_bf-Diameter of basalt fibers (µm), V_bf-Volume content of basalt fibers (%) and Cs_bf-Compressive strength of basalt fibre concrete (MPa). The collected records were divided into training set (249 records≈80%) and validation set (60 records≈ 20%). At the end of the process, it can be shown that the present research work outclassed other ML techniques applied in the previous research paper, which reported the utilization of the same size of data entries and basalt reinforced concrete constituents. Taylor chart for measured compressive strength of basalt fiber reinforced concrete predicted with ANN, KNN, SVM, Tree and RF is presented for comparing the performance of predictive models by illustrating three key statistical measures simultaneously: the correlation coefficient (R), the normalized standard deviation (σ), and the root-mean-square error (RMSE). Finally, it can be deduced that after considering the performance indices of the selected ensemble and classification models utilized in this present research paper, all the developed modes have almost the same excellent level of accuracy 95%, but ANN, KNN, and SVR produced R2 of 0.98 each with KNN producing MAE of 1.4 MPa, and MSE of 2.5 MPa to outperform ANN and SVR which produced MAE of 1.55 MPa/MSE of 4.1 MPa and MAE of 1.6 MPa/MSE of 3.85 MPa, respectively. Three techniques were used to estimate the impact of each input on the compressive strength, namely correlation matrix, sensitivity analysis and relative importance chart.
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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Imran, Hamza
This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). The study evaluates these models based on their ability to predict compressive strength (Fc), tensile strength (Ft), density, and slump using key statistical performance indicators such as SSE, MAE, MSE, RMSE, accuracy, R2, and KGE. Sensitivity analysis was conducted using Hoffman and Gardener’s method as well as the SHAP technique to determine the most influential parameter in the prediction process. Results indicate that the Firefly and Wolf algorithms exhibited the highest prediction accuracy across all four properties, with Wolf emerging as the overall best-performing model due to its superior generalization ability, lower error rates, and high correlation with experimental results. Among the input parameters, the water-to-binder ratio was identified as the most influential factor affecting the mechanical properties of waste glass aggregate concrete, as demonstrated by both sensitivity analysis methods. This highlights the critical role of optimal water content in achieving desirable strength and workability in sustainable concrete mixtures. The study’s novelty lies in the comparative assessment of multiple optimization algorithms applied to waste-based concrete, an approach that has not been extensively explored in previous research. Additionally, the integration of SHAP analysis for feature importance ranking provides an interpretable machine learning approach to concrete mix design, which enhances decision-making for engineers and researchers. The practical implications of this research extend to sustainable machine learning-based concrete design, where AI-driven optimization can help reduce the reliance on conventional trial-and-error methods. By utilizing waste glass aggregates, the study supports circular economy initiatives in construction, reducing environmental impact while maintaining structural performance. The proposed models can be implemented in real-world scenarios to optimize mix designs for large-scale applications, leading to cost-effective and eco-friendly construction materials. This research advances the field of smart construction by demonstrating the effectiveness of machine learning in sustainable material engineering, paving the way for future AI-assisted innovations in the industry.
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Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions
(2025-12-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Hanandeh, Shadi
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Llamuca Llamuca, José Luis
The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promising approaches for this task due to their complementary strengths and solving challenges associated with repeated experiments in the laboratory. Choosing machine learning predictions over repeated, expensive, and time-consuming experiments in research projects, such as optimizing the utilization of metakaolin in pre-cured geopolymer concrete, presents a paradigm shift in how data-driven insights can revolutionize material development. The integration of ensemble and symbolic regression models enables researchers to derive valuable predictions and optimize critical performance parameters efficiently. In this research work, 235 records were collected from extensive literature search for compressive strength for different mixing ratios of pre-cured metakaolin-based geopolymer concrete with concrete at different ages. Each record contains MK: The content of metakaolin (kg/m3), SHS: Sodium hydroxide solution content (kg/m3), SHSM: Sodium hydroxide solution molarity (Mole), SSS: Sodium silicate solution content (kg/m3), W: Extra water content (not including the water in alkaline solutions) (kg/m3), W/S: Water to Solid ratio (Total water content / Solid part of activator solutions + MK), Na<inf>2</inf>O/Al<inf>2</inf>O<inf>3</inf>: Sodium oxide to aluminium oxide ratio, SiO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf>: Silicon oxide to aluminium oxide ratio, H<inf>2</inf>O/Na<inf>2</inf>O: Water to Sodium oxide ratio, CA/FA: Coarse to Fine aggregate ratio, CAg: The content of coarse aggregates (kg/m3), SP: The content of super-plasticizer (kg/m3), PCC: 0 for no pre-curing, 1 for pre-curing at 60 °C, and 2 for pre-curing at 80 °C, CT: Curing temperature (°C), Age: The concrete age at testing (days) and CS: Compressive strength (MPa). The collected records were portioned into training set (180 records≈75%) and validation set (55 records≈ 25%) and modeled with ensemble and symbolic regression methods. At the end of the model work, performance metrics were used to evaluate the models’ ability and Hoffman and Gardener’s sensitivity analysis was used to evaluate the impact of the variables on the compressive strength of the pre-cured geopolymer concrete mixed with metakaolin. GB and KNN models became the decisive models with excellent performance which outclassed others and the sensitivity analysis indicated that SHSM, SSS, W/S, and Na<inf>2</inf>O/Al<inf>2</inf>O<inf>3</inf> are the most influential to the predicted compressive strength.
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Fractional-order modeling of dengue dynamics: exploring reinfection mechanisms with the Atangana–Baleanu derivative
(2025-08-01)
Lamwong, Jiraporn
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Pongsumpun, Puntani
Dengue fever poses ongoing public health challenges due to its complex reinfection dynamics and antibody-dependent enhancement (ADE). To address limitations in classical models, this study proposes a novel fractional-order model utilizing the Atangana–Baleanu–Caputo derivative to capture memory and non-local effects inherent in dengue transmission. The model explicitly incorporates reinfection mechanisms and stages of infection, offering a more accurate depiction of disease progression. The existence and uniqueness of solutions are established using fixed-point theory, and the global stability of equilibria is analyzed via Lyapunov methods. Model fitting with real-world data from Thailand in 2023 confirms predictive accuracy, while sensitivity analysis identifies the biting and mosquito mortality rates as critical parameters influencing the basic reproduction number. This framework enhances the realism of epidemic models and provides actionable insights for designing targeted public health interventions in dengue-endemic regions.
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Atangana-Baleanu fractional optimal control for dengue dynamics with stability analysis
(2025-08-01)
Lamwong, Jiraporn
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Pongsumpun, Puntani
Dengue fever remains a critical public health concern, particularly in regions like Thailand, where the disease exhibits complex transmission dynamics involving human and mosquito populations. Traditional models often fail to address the intricacies of non-local interactions, memory effects, and control dynamics. This research introduces an innovative approach using fractional optimal control problems (FOCPs) integrated with the Atangana-Baleanu fractional derivative in the Caputo sense. The model stratifies human and mosquito populations into detailed compartments, enabling a granular representation of transmission dynamics. The FOCP framework leverages fractional-order equations to incorporate memory-dependent and non-local interactions, ensuring biological feasibility and predictive accuracy. Computational results reveal that the model aligns closely with observed data for dengue fever, dengue hemorrhagic fever, and dengue shock syndrome across fractional orders ranging from 0.83 to 1.00. Sensitivity analyses identify critical parameters, such as biting rates and initial population sizes, as pivotal to disease control. The findings underscore the effectiveness of FOCPs in optimizing public health interventions, offer a robust tool for minimizing infection rates and associated costs. The theoretical global stability analysis confirms the model's reliability in predicting long-term outcomes under varying epidemiological scenarios. Future research could extend this framework to incorporate environmental variables, co-infections, and vaccination strategies, enhancing its applicability across diverse public health challenges. This study represents a significant step forward in the mathematical modeling of epidemic diseases, particularly in optimizing control measures for dengue fever.
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A fractional derivative model of the dynamic of dengue transmission based on seasonal factors in Thailand
(2025-03-15)
Lamwong, Jiraporn
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Pongsumpun, Puntani
Climate variability affects the changes in controlling diseases transferred by insects. An increase in the population, the growth of communities, and a lack of public health infrastructure bring about the return of diseases of which insects are carriers, one of the illness issues. Therefore, the disease control is significant to help reduce the burden on the government and strengthen the country's public health structure. This research proposes a novel approach to modeling dengue fever dynamics, we employ a fractional derivative model with the Atangana–Baleanu–Caputo derivative, which offers a more accurate representation of real-world disease dynamics compared to traditional integer-order models. Basic qualifications are proposed. Equilibrium points and basic reproduction numbers are analyzed. The next-generation matrix method is used to identify the transmission. Besides, parameter sensitivity analysis is performed to learn about factors affecting input parameter values' effects on the basic reproduction number. It was found that the most common parameter affecting the transmission was the biting rate of mosquitoes was 1. In addition, the existence and uniqueness of the solution are examined using the Banach fixed point theorem. The Toufik–Atangana method is used for the numerical examination of a fractional version of the proposed model. We compared different values of fractional-order α=0.965, 0.975, 0.985, 0.995 and 1 it was found that when the order of derivatives decreases, the transmission shall decrease accordingly. This research provides valuable insights for developing effective control strategies to reduce the burden of dengue fever and strengthen public health systems.

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