Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.
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Modeling HIV and AIDS data in Trenggalek and Ponorogo with a bivariate zero-inflated Poisson approach
The presence of excess zeros and overdispersion in count data often leads to biased parameter estimates when analyzed using the standard Poisson regression model. This study aims to model the number of HIV and AIDS cases in Trenggalek and Ponorogo Regencies using the Bivariate Zero-Inflated Poisson (BZIP) regression approach. The BZIP model accommodates correlated count responses as well as excess zeros, commonly found in epidemiological data. Two response variables, the number of HIV cases (Y₁) and the number of AIDS cases (Y₂), were analyzed against five explanatory variables: the percentage of the population aged 25–29 years (X₁), low education level (X₂), condom use among couples of reproductive ages (X₃), participation in health education programs (X₄), and community health insurance coverage (X₅). Parameter estimation was performed using the Expectation-Maximization (EM) algorithm. The results show that health education significantly increases the likelihood that an area has no HIV cases, while health insurance significantly reduces the number of AIDS cases. Moreover, individuals aged 25–29 years were identified as the group most at risk for AIDS. The model also confirmed strong overdispersion and zero inflation, supporting the use of BZIP as a more appropriate model than the standard bivariate Poisson regression. Additionally, the BZIP model achieved the best performance, indicated by the lowest AIC value compared to previous models.
Topic-based sentiment analysis of Jamsostek mobile application reviews using the BERTopic method
Rapid developments in digital technology have changed the way public services are delivered, shifting from conventional systems to mobile-based platforms. BPJS Ketenagakerjaan has adopted this transformation through the development of the Jamsostek Mobile (JMO) application. The application can be accessed on the Google Play Store, and users can provide ratings and reviews on the Google Play Store page. To improve the quality of the JMO application, it is important to pay attention to these reviews. Therefore, sentiment analysis is needed to identify and analyse positive and negative sentiments related to the use of the JMO application. The method used combines the IndoBERT and BERTopic models to understand user opinions in greater depth. This study shows that 75% of 2,846 comments contain negative sentiments, and the IndoBERT model achieves 100% accuracy in sentiment classification. In topic modelling, five clusters were formed, with the most discussed topics being requests for app updates and users' difficulties in accessing the JMO app due to constant errors and slowness.
Modeling asymmetric volatility with long memory effect using a FIGARCH ANN approach: evidence from ANTM stock
This study models the volatility of daily returns for PT Aneka Tambang Tbk (ANTM), which exhibits volatility clustering, fat tails, asymmetry, and long memory. The analysis proceeds in two stages: (i) conditional variance modeling using FIGARCH (1, d, 1) to represent long-memory dynamics; and (ii) design of a FIGARCH ANN hybrid (backpropagation) to absorb residual nonlinearity/asymmetry. Preprocessing tests confirm stationarity in log returns, followed by ARIMA baseline selection and confirmation of conditional heteroskedasticity in the residuals. Long-memory estimation via the GPH procedure confirms statistically significant long memory (past shocks have persistent effects). Compared to GARCH (1,1) and EGARCH, FIGARCH provides a better fit because it has the smallest AIC/BIC value. Residual diagnostics for FIGARCH are clean, indicating no autocorrelation and no remaining ARCH effects the model captures the main volatility structure. Ten steps ahead forecasts show the conditional variance stabilizing, implying that shocks decay slowly (persistence) toward a relatively stable level. The ANN component trained on residuals/logvariance reduces error metrics (MSE/RMSE/MAE) compared with standalone FIGARCH, evidencing the benefit of nonlinear correction for short horizon accuracy.
Hybrid MobileNetV3-LSTM model for detecting phasic and tonic receptor responses in facial images
Pain was a complex and subjective experience that involved sensory, emotional, and cognitive aspects simultaneously. The responses of phasic and tonic receptors to painful stimuli exhibited different patterns and could be observed through facial expressions as a form of nonverbal communication. This study aimed to implement a MobileNetV3-LSTM model to classify phasic, tonic, and normal receptor responses using human facial expression images. The objective was to obtain the most optimal model for classifying facial expressions exposed to pain stimuli targeting phasic or tonic receptors. The methods involved the development and evaluation of three models: MobileNetV3Large, MobileNetV3Small, and their respective hybrid versions combined with LSTM, to examine the effect of incorporating temporal information on classification performance. According to results 10, the hybrid MobileNetV3Large-LSTM model performed the best, with an F1-score of 94%, accuracy of 93%, precision of 96%, and recall of 93% on the 12 test data. Meanwhile, the MobileNetV3Small-LSTM model reached 74% accuracy, 80% precision, 74% recall, and a 74% F1-score. The standalone MobileNetV3Large model only obtained 68% accuracy and an F1-score of 0.59, while MobileNetV3Small without LSTM achieved 75% accuracy and an F1-score of 0.56. These results suggest that the inclusion of LSTM layers greatly enhanced the accuracy in the model. This research added to the development of facial expression classification methodologies to recognize pain and complemented the body of knowledge on hybrid model utilization in deep learning.
Decadal rainfall forecasting using CNN–BiLSTM: a case study in Indramayu, Indonesia
Failures in rice cultivation often result from extreme rainfall variability—ranging from droughts due to low precipitation to crop damage caused by excessive rainfall. This makes rice production highly sensitive to climate fluctuations, emphasizing the need for accurate rainfall forecasting to optimize planting and harvesting schedules. In Indramayu Regency, unpredictable rainfall patterns pose significant forecasting challenges. With the Indonesian Meteorological Agency’s (BMKG) average accuracy reaching only 63%, alternative approaches are urgently needed. This study employs a hybrid deep learning model—Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM)—to address these limitations, as previous methods such as RNN and LSTM have struggled to capture seasonal patterns and the complexity of decadal rainfall data. The model was trained on rainfall data from January 2000 to July 2022 and tested on data from August 2022 to January 2025. The model architecture includes two convolutional layers (filters 16 and 32), max pooling, and three Bi-LSTM layers (64, 50, and 32 neurons), trained using the Adam optimizer (learning rate = 0.0001), a batch size of 64, and Mean Square Error (MSE) as the loss function. Evaluation results indicate a Mean Absolute Percentage Error (MAPE) of 17.49%, classified as “good” forecasting accuracy. This translates to an overall accuracy of 82.51% (MAPE-based classification). These findings demonstrate that the CNN-BiLSTM model effectively predicts decadal rainfall in Indramayu and has the potential to reduce crop loss by optimizing agricultural strategies such as drainage, fertilization, and harvesting aligned with rainfall projections for February to April 2025.
Residual-based unsupervised bearing fault detection using ICA-enhanced LSTM autoencoder
Early fault detection in rolling element bearings remains a challenging problem, particularly under unsupervised conditions where labeled fault data are unavailable. Incipient defects often generate weak impulsive vibration signatures that are easily masked by operational noise. This paper proposes a residual-based unsupervised fault detection method that integrates Independent Component Analysis (ICA) with a Long Short-Term Memory (LSTM) autoencoder. ICA is employed to decompose vibration signals into statistically independent components, enhancing fault-related impulsive features while suppressing redundant background vibration. An LSTM autoencoder is trained exclusively on healthy-condition data to learn normal temporal dynamics. Bearing anomalies are identified through reconstruction residuals, which quantify deviations from learned healthy behavior. Instead of fixed or heuristic thresholds, the decision boundary is determined via F1-score optimization, framing fault detection as a data-driven residual decision problem. The proposed approach is validated using the Case Western Reserve University (CWRU) bearing dataset under a 2 hp load condition, focusing on inner race faults. Experimental results demonstrate perfect fault recall and an ROC-AUC of 1.000, confirming the effectiveness of ICA-enhanced residual learning for early fault detection. The method is computationally efficient, interpretable, and suitable for practical predictive maintenance applications.
Plant and disease classification with explainable AI in web-based application
The application of deep learning for plant and disease recognition has become increasingly popular; however, most studies address these tasks separately due to limitations in dataset availability for model training and testing. This study aims to overcome such constraints by developing a multi-output classification framework that simultaneously predicts both plant species and associated diseases. Three state-of-the-art architectures were employed: NASNetMobile, a CNN-based model; a hybrid CNN-LSTM; and a CNN integrated with an Attention mechanism. A combined dataset of over 15.678 images was compiled from multiple public sources, covering 10 plant species and 27 disease classes with diverse real-world conditions. The training process was done using two different approaches: with and without data augmentation. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that NASNetMobile without data augmentation achieved the highest performance, with an F1-score of 99.79% for plant classification and 98.54% for disease classification, outperforming CNN-LSTM (98.86% and 95.2%) and CNN-Attention (98.65% and 93.3%). These findings demonstrate that lightweight yet robust architectures such as NASNetMobile can effectively bridge the gap between laboratory-trained models and field-ready agricultural applications, supporting the advancement of precision agriculture. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and Eigen-CAM were applied, providing intuitive visualizations that help users understand model predictions. The best-performing model was deployed in a web-based proof-of-concept application, developed using Streamlit. This work provides one of the first multi-output explainable frameworks for plant and disease classification deployable in a web-based system.
Classification of essential and non-essential genes in human genome sequence data using ensemble machine learning
DNA (Deoxyribonucleic Acid), RNA (Ribonucleic Acid), and proteins are basic biochemical molecules essential for cellular organization. DNA serves as the primary store of genetic information encoded in genes, and humans are estimated to have 20,000 to 30,000 genes. This genetic information is represented in a chemical code. Essential genes are fundamental to various biological mechanisms, and when disrupted or removed, they may cause genetic defects, mutations, or, in extreme conditions, result in organismal fatality. Identifying these genes through experimental methods requires large resources and is often inefficient. Computational methods, especially those involving machine learning, offer a more efficient and effective solution to this challenge. This research explores the ability of machine learning techniques to build classification models for human gene sequence data. Two data sets, Cellular Essential Gene (CEG) and Organism Essential Gene (OEG), were analyzed, with genes categorized as essential or non-essential. The study was structured through multiple phases, such as data acquisition, cleaning, feature engineering, and dividing the dataset into subsets for model training and evaluation. Model construction followed this phase, where various ensemble learning techniques were applied. These included algorithms like Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Adaptive Boosting. The best overall results were achieved using the SVM model with 5-mers, reaching a sensitivity of 0.81 and ROC AUC of 0.81 on the CEG dataset, and a PR AUC of 0.46, sensitivity of 0.69, and ROC AUC of 0.80 on the OEG dataset. The consistently high accuracy results indicate that these models effectively distinguish essential and non-essential genes. These machine learning-based classification models can potentially be valuable tools in the healthcare field, contributing to a deeper understanding of normal gene function in organisms.
Outlier detection in multivariate time series: an application of hybrid DNN-DBSCAN technique
In spite of on-going advances and utilization of Deep Neural Networks (DNN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) techniques to enhance outlier detection in multivariate time series (MTS) data, research is yet to explore an approach that integrates the capabilities of the two techniques for complex data representation. The paper therefore combines the two techniques to obtain the hybrid DNN-DBSCAN technique. It is demonstrated in simulated data that the resulting technique achieves improved precision and recall of outlier detection based on a number of performance metrics. In particular, the new procedure adequately captures the complexity involved with the underlying high-dimensionality of MTS data which poses problems for outlier detection to traditional methods.
Modeling the impact of public opinion and preventive practices on malaria transmission in central and northern Benin
Malaria remains a major public health challenge in Sub-Saharan Africa, especially in Northern Benin, where seasonal variations drive transmission. The success of prevention and treatment depends on community compliance, which is influenced by public risk perception and social attitudes. Despite the widespread implementation of preventive interventions, malaria transmission persists at alarming levels in many regions. Existing mathematical models have often overlooked the role of population opinions and behavioral responses in shaping the effectiveness of these interventions. To address this gap, we extended a mathematical model integrating malaria transmission dynamics with an opinion dynamics framework. The study was conducted across four Benin districts, using five years (2019–2023) of real-world malaria surveillance data from the National Malaria Control Program. Behavioral data were derived from the 2022 Malaria Behavior Survey. The model was calibrated using nonlinear least squares estimation techniques. Analytical results confirm the positivity and boundedness of the model, and a disease-free periodic solution was established. The control reproduction number (Rc) was computed using the monodromy matrix method. The numerical analysis revealed that increased the percentage of favorable adherence to prophylactic measures results in a slight but consistent decrease in malaria incidence. Specifically, in Bante, when partial adherence rose from 52% to 100%, the effective reproduction number decreased by 82.75%. Furthermore, we also note that a higher baseline influence rate (Ω0) contributed to a substantial reduction in effective reproduction number. In Sinende, increasing Ω0 from 0.05 to 50 reduced malaria incidence by 17.65%. The findings highlight that incorporating population behaviors and opinions into disease modeling enhances the effectiveness of public health strategies for sustainable malaria control in endemic areas.
Optimal control on model of COVID-19 with ineffective vaccination and quarantine
In this paper, we present an optimal control approach for an (SEIRV) epidemic model of COVID-19 disease by controlling quarantine measures on susceptible individuals and controlling the vaccination rate for susceptible individuals, exposed but not yet infectious individuals, and asymptomatic infectious individuals to reduce the disease burden and related costs. We have proven that an optimal control does exist, and we used Pontryagin’s maximum principle to characterize the optimal control. In numerical simulations, we solve the optimal control problem by the fourth-order Runge–Kutta method. Moreover, we discuss different cases for optimal control of quarantine measures and vaccination quantity presented through graphical representations.
Time-Delay-Induced Hopf Bifurcation in a Fractional-Order Ecological System
In this paper, we investigate a fractional-order delayed dynamical system. Taking the time delay τ as the bifurcation parameter, we establish that the equilibrium is asymptotically stable for all τ<τ0, and a Hopf bifurcation occurs as τ passes through the critical value τ0. We derive the characteristic equation and verify the transversality condition, which identifies the onset of oscillations and characterizes the stability of the emerging periodic solutions. Numerical experiments carried out in MATLAB with a predictor–corrector scheme support the analysis and illustrate the resulting oscillatory behavior.
Global dynamics of a multi-stage fractional HIV-infected model with general incidence rate and drug efficacy
For several decades researchers have been studying global dynamics of viral infection models to prevent wide outbreak of scattered virus both in population and vivo such as Dengue fever, SARS-COV2, HRSV, and immunodeficiency diseases. In this paper, our main aim is obtaining sufficient conditions for the global stability of equilibria of a Caputo fractional derivative order system with general incidence functional response by using Lyapunov’s method and LaSalle’s invariance principle. We prove the global stability of stationary points by the values of the basic reproduction number (R0) and we consider this threshold as a strong index for our sensitivity analysis. We confirm theoretical results through numerical simulations.
Brainprint authentication under varying environmental conditions: machine learning versus deep learning
This study explores the potential of using EEG signals for biometric authentication through the development of Convolutional Neural Network (CNN) model. In particular, the electroencephalogram (EEG) signals were recorded in different ambient noise settings: quiet environment, low-distraction environment, and high-distraction environment. Traditionally, EEG-based authentication requires a separate feature extraction step prior to the use of machine learning algorithm. Feature extraction process is usually cumbersome, which relies heavily on human experts, and prone to information loss. Thus, a CNN model based on EEGNet architecture is proposed to train EEG datasets collected from 45 volunteers who were instructed to look at images presented to them in all the three acoustic conditions. Using a variety of performance metrics, notably precision and recall, the model’s performance was compared across various classification thresholds to account for the imbalanced nature of the dataset. The performance was also compared across different environmental conditions, with the highest F1-score in quiet conditions. Additionally, the CNN’s performance was compared against a probability-based Incremental Fuzzy-Rough Nearest Neighbour (prob-IncFRNN) model, with former outperforming the latter in all metrics.
Mathematical model analysis of stages of cervical cancer with the side effect hematological toxicity in radio-chemotherapy
Cervical cancer is the fourth most common cancer in women worldwide. Cancer treatments are often used to kill or remove tumors or metastases, reduce the risk of recurrence, and slow the development of tumors or metastases. They are also used to prevent and manage symptoms and complications caused by the disease and its treatments, ensuring the best possible quality of life. However, the effects of cancer treatments on patients are not always positive. The treatment of cervical cancer using a combination of radiotherapy and chemotherapy affects red and white blood cells as well as plasma. These side effects, known as hematological toxicity, led us to construct a mathematical model based on a population of hospitalized cervical cancer patients. This population is divided into five sub-populations classified according to disease stages, and the model is built using a system of differential equations. We analyzed the equilibrium points and performed stability analysis to study the population’s dynamics over time. Finally, we conducted numerical simulations to verify the results of the analysis.
The stability and random attractors of the stochastic model describing the oncolytic virotherapy with tumor-virus interactions
The treatment of cancer tumors varies based on cancer type, stage, location, and patient health. This research focuses on viral therapy, which employs viruses that target and eliminate malignant cells while sparing healthy ones. A stochastic model investigates suicidal viral tumor therapy, emphasizing the interaction between tumorous and non-tumorous cells. The study analyzes the stochastic dynamics and stability of the system's equilibrium points through stochastic differential equations. Key model factors affecting viral treatment outcomes are identified through bifurcation parameters and sensitivity analysis, validated by numerical simulation.
The influence of hunting cooperation, fear and anti-predator on the dynamic of a prey-predator model with disease in prey
In this study, we examine an eco-epidemiological prey-predator model that incorporates hunting cooperation, fear, and anti-predator behavior including linear harvest. There are two subclasses of prey: susceptible and diseased. We investigate thorough mathematical analysis, including the presence and stability of equilibria, the boundedness of the model, and the existence and uniqueness of solutions. The conditions under which local bifurcation could occur near the equilibrium points were discovered. Numerical simulations were run to validate the model's long-term behavior and comprehend the impact of the model's main parameters. The purpose is to demonstrate the analytical findings numerically and study the impact of changing the parameters on the dynamical behavior of the system, and control settings are determined by numerical simulations using MATLAB, R2021a.
Transformation of pellet-based planting media as an organic matter and nutrient enhancer for improving soil quality
Soil degradation driven by excessive inorganic fertilizer use and conventional cultivation practices has reduced land productivity in tropical regions, including Indonesia. This study developed biological pellet-based planting media to improve soil physical, chemical, and biological properties while providing controlled nutrient release. The research employed a panel design and split-plot layout on sandy Entisol soils, evaluating physical–chemical characteristics, rhizosphere microbial colonization, and nutrient-release kinetics using the Gompertz model. The results show that pellets increased soil porosity up to 56%, extended water retention to 22 hours, and significantly enhanced organic carbon as well as N, P, K, Ca, and Mg levels compared with the control. Nutrient release followed a lag–exponential–plateau pattern, indicating an efficient gradual-release mechanism. Microbial and soil-fauna activity more than doubled, demonstrating restored biological functioning of the soil. Treatments with a balanced organic–inorganic ratio (50:50) exhibited the best overall performance across all parameters. Overall, pellet-based planting media prove effective as multifunctional soil conditioners that enhance fertility, nutrient efficiency, and agroecosystem sustainability in marginal tropical soils.
Modeling and evaluating targeted interventions to curb avian influenza spread among humans and domestic birds
Avian influenza, caused by influenza A viruses, has drawn substantial attention globally due to increased deaths from time to time. This study examines control interventions for avian influenza transmission in humans and domestic birds using a deterministic mathematical model. The model incorporates three time-dependent interventions: vaccinating susceptible birds, maintaining proper hygiene practice, and culling infected birds. The next-generation matrix method is used to determine the effective reproduction number. Optimal control theory is applied by incorporating: vaccination, proper human hygiene practices, and culling of infected birds. To determine the necessary conditions for the existence of optimal controls, the Pontryagin’s Maximum Principle is employed. The optimal control problem is then solved using the forward-backward sweep method based on the fourth-order Runge-Kutta algorithm, implemented in MATLAB. Results from the optimal control analysis show that the strategy combining all interventions is the most effective approach for controlling the disease. Furthermore, the Incremental Cost-Effectiveness Ratio (ICER) is used to identify the most cost-effective strategy for disease control. The findings show that Strategy VII has a negative ICER, indicating that it is less costly and averts more infections. Therefore, to eliminate the disease, we recommend the implementation of all the aforementioned control measures.
Concrete crack segmentation using U-Net based models with various pre-trained backbones
The use of deep learning to detect and analyze structural damage in concrete is gaining increasing attention, but exploration of U-Net with various pre-trained backbone models has not been conducted. This study aims to overcome these limitations by developing a U-Net-based concrete crack segmentation framework that utilizes eight modern and lightweight backbones, namely EfficientNetB0, VGG16, VGG19, ResNet34, ResNet50, DenseNet121, MobileNetV2, InceptionV3, and Xception. A total of 458 labeled images from the Concrete Crack Segmentation dataset were used as training and testing data, representing the variety of concrete surface conditions in real environments. Evaluation was conducted using Dice and Intersection over Union (IoU) metrics. The test results showed that the Xception backbone provided the best performance, with a Dice value of 0.8461 and an IoU of 0.7384, surpassing EfficientNetB0 and VGG16/VGG19 which were previously considered stable in segmentation tasks. This finding confirms that the depthwise separable convolution mechanism in Xception is capable of extracting thin crack features more representatively. This study provides an important contribution in selecting the optimal backbone for concrete crack segmentation models, while opening up opportunities for implementing more accurate and efficient structural condition monitoring technology on an industrial and public infrastructure scale.
Bifurcation and persistence conditions of Sokol-Howell prey-predator model with toxins and additional food for predators
This work investigates the conditions for local bifurcation in a three-species food chain model, comprising prey, predator, and top predator, incorporating factors such as fear, a weak Allee effect, toxins, and supplementary food in the predator population, utilizing the Sokol-Howell functional response. This system possesses thirteen equilibrium points. Transcritical and pitchfork bifurcations occur near the extinction equilibrium point (Q0), the predator-top predator-free equilibrium point (Q1), the prey-top predator-free equilibrium point (Q3), while near and the top predator-free equilibrium point (Q4), while saddle node bifurcations are found near the prey-free equilibrium point (Q8), and the interior equilibrium point (Q10). Moreover, the persistence conditions have been derived to ensure the coexistence and stability of all species. Numerical simulation performed using matlab support the analytic finding and illustrate the complex dynamic behaviors near the equilibrium points. This study provides deeper insight into how Allee effect, toxin effects and additional food sources influence the bifurcation structure and persistence of the three-species ecological system.
Water potability classification using machine learning: a case study on handling incomplete data
Water is essential for the preservation of life on Earth, particularly for drinking purposes. Despite the abundance of water in the earth's ecosystem, the world is currently grappling with a significant global issue of contaminated water, a problem that extends beyond natural contamination and includes industrial wastewater. In this study, we aimed to investigate the potential of decision tree-based machine learning models, including decision trees, ensemble boosting, ensemble bagging, and random forests, in predicting water potability based on specific parameters. We used publicly available data from the "Water Quality and Potability" Kaggle dataset. Due to the high number of missing values for some parameters in the dataset, our research initially converted continuous, missing values into discrete or categorical values. We then filled these gaps with a general label, "unknown," instead of using mean or median values as other studies had done. Initially the result showed that the highest accuracy was random forest; our analysts showed that the sulfate parameters created confusion for the machine learning model due to the many missing values. So that, this research decided to exclude sulfate data from the dataset, and this research showed significant results where all decision tree-based machine learning models can achieve 100% accuracy, precision, recall, and f1-score on evaluation using the test dataset.
Data-driven approach for temperature and relative humidity forecasting in solar dryer dome facility
Accurate prediction of temperature and relative humidity is essential in Solar Dryer Dome (SDD) facilities to ensure strict quality of the agricultural products. Compared to traditional control models, ML-based approaches provide benefits in terms of adaptability, as they are capable of capturing complex and non-linear patterns in time series data. In this project, primary time series datasets collected from SDD facility in North Jakarta were used to forecast the next hour temperature and relative humidity values within the 5-minutes resolution. There were three sensors placed in three different locations: outside the facility near the front door, inside the facility near the front door, and near the fan inlet. The collected data showed daily seasonality patterns with temperature and relative humidity possess an inversely correlated relationship due to their inherent physical interaction. On the modeling part, this research compared three different predictive models such as XGBoost, Linear Regression, and Facebook’s Prophet based on the RMSE and R2 scores on the test set. XGBoost and Linear Regression provided on-par performance, with XGBoost showing its best at temperature prediction (RMSE: 1.47°C, R2: 0.96) and Linear Regression showing its best at relative humidity prediction (RMSE: 3.20%, R2: 0.96). Meanwhile, Prophet exhibits the lowest performance in both variables with RMSE: 3.56°C and R2: 0.72 for temperature, and RMSE: 8.42% and R2: 0.76 for relative humidity forecasting. Despite the low performance, the Prophet model was advantageous in terms of consistency across horizons, while the two best models show decreasing performance in higher horizon settings. These findings suggest the promising application of ML techniques in SDD facilities to support sustainability in agro-industrial business.
Vector basis S-cordial labeling of graphs with applications in biological models
Let \(G\) be a \((p,q)\) graph. Let \(V\) be an inner product space with basis \(S\). We denote the inner product of the vectors \(x\) and \(y\) by \(<x,y>\). Let \(\phi: V(G) \rightarrow S\) be a function. For edge \(uv\) assign the label \(<\phi(u),\phi(v)>\). Then \(\phi\) is called a vector basis \(S\)-cordial labeling of \(G\) if \(|\phi_{x}-\phi_{y}|\leq 1\) and \(|\gamma_i-\gamma_j |\leq 1\) where \(\phi_{x}\) denotes the number of vertices labeled with the vector \(x\) and \(\gamma_i\) denotes the number of edges labeled with the scalar \(i\). A graph which admits a vector basis \(S\)-cordial labeling is called a vector basis \(S\)-cordial graph. In this paper, we examine the vector basis {(1,1,1,1),(1,1,1,0),(1,1,0,0),(1,0,0,0)}-cordial labeling behaviour of Mongolian tent and parachute graph such a labeling finds applications in biological modelling.
Robust spatial Durbin model for dengue fever cases in Indonesia
Spatial regression analysis is a method employed for data exhibiting spatial dependencies. The Spatial Durbin Model (SDM) is one such method that accounts for spatial effects in both the dependent and independent variables. However, the presence of spatial outliers can compromise the accuracy of SDM predictions. To address this issue, a robust method is needed, namely the Robust Spatial Durbin Model (RSDM). This study applies the RSDM to model the factors influencing the spread of dengue haemorrhagic fever cases across Indonesia and to identify the superior modeling approach. The results indicate that the RSDM outperforms the standard SDM, evidenced by a higher Adjusted R² and a lower Mean Squared Error (MSE). Key factors identified as significantly influencing dengue haemorrhagic fever cases are population density, the number of doctors in healthcare facilities, and the percentage of the population covered by health insurance.
A comparative analysis of kernel filtering techniques for convolutional neural networks in fingerprint recognition
Fingerprint recognition remains one of the most widely adopted biometric methods for individual verification due to its uniqueness and reliability. This study investigates the role of kernel-based filtering in the feature extraction stage and evaluates the performance of four Convolutional Neural Network (CNN) architectures: Siamese Network, LeNet, ResNet, and VGG-19. Experiments were conducted on the SOCOFing dataset, with preprocessing steps involving image resizing, normalization, and augmentation. To enhance the discriminative quality of fingerprint features, Sobel, Gabor, Laplacian, and Gaussian kernels were applied prior to model training. Model performance was assessed using accuracy, loss, and the area under the ROC curve (AUC), with AUC emphasized as the primary metric to address class imbalance. The results demonstrate that Gaussian and Sobel kernels consistently yield superior and stable performance across all models. Furthermore, statistical validation through Friedman and Nemenyi tests confirmed significant differences among kernel–model combinations, underscoring the benefit of multi-kernel preprocessing in biometric classification. Overall, the findings highlight the critical role of kernel selection in optimizing fingerprint recognition systems, with Gaussian filtering showing particular promise for enhancing adaptability, robustness, and classification accuracy.
A fractional-order spatial-temporal model for the adoption dynamics of remote healthcare services
This paper presents a fractional-order (FO) spatial-temporal model for the adoption dynamics of Remote Healthcare Services (RHS) in a two-dimensional domain. The model incorporates memory effects and non-local temporal interactions through the Caputo nabla fractional derivative (CNFD), providing a realistic framework for social contagion processes where past states influence current behavior. The spatial domain is discretized using the Method of Lines (MOL) with periodic boundary conditions, transforming the original partial differential equations (PDEs) into a system of fractional ordinary differential equations (ODEs). We derive the equilibrium points (EPs) of the system and establish sufficient conditions for global asymptotic stability (GAS) via a Lyapunov functional (LF) approach. Numerical simulations validate the theoretical stability results, demonstrating convergence to the trivial equilibrium under the chosen parameter set. The proposed model offers a robust framework for analyzing spatial-temporal adoption dynamics with potential applications in public health, technology diffusion, and social behavior modeling.
Trends of malnutrition and contributing factors among children under five years in Cameroon: lessons from four rounds of DHS data
Introduction: Malnutrition among children under five remains a pressing public health issue in Cameroon, contributing to high rates of morbidity and mortality. Understanding the factors influencing malnutrition over time is essential for designing effective, targeted interventions. Methods: This study uses data from four rounds of the Cameroon Demographic and Health Survey (CDHS) conducted in 2004, 2011, 2018, and 2022. Anthropometric indicators were used to assess nutritional outcomes, specifically stunting, wasting, and underweight. Bivariate analyses (Chi-square tests) and multivariate logistic regression models were employed to identify significant socioeconomic, demographic, and environmental predictors of malnutrition. Results: The prevalence of stunting declined from 35% in 2004 to 23% in 2022, reflecting gradual improvements in child nutrition. Despite this positive trend, malnutrition remains a significant concern. Key determinants of poor nutritional outcomes include low household wealth, limited maternal education, short birth intervals, lack of antenatal care, and rural residence. Conclusion: While there has been notable progress in reducing child malnutrition, it continues to pose a serious public health challenge in Cameroon. The study highlights the need for integrated, multisectoral strategies that address both direct child-related factors and broader household and maternal conditions. Policies aiming to improve maternal education, access to healthcare, and living standards are critical for further reducing malnutrition rates and enhancing child well-being.
Evaluation of the performance of LASSO, group LASSO, and sparse group LASSO in identifying factors that have a significant influence on dengue hemorrhagic fever in Indonesia
Dengue Hemorrhagic Fever (DHF) remains a major public health challenge in Indonesia, driven by complex interactions among climatic, demographic, socio-economic, health capacity, and environmental factors. Identifying significant determinants in high-dimensional data with grouped explanatory variables requires appropriate regularization techniques. This study evaluates and compares the performance of LASSO, Group LASSO, and Sparse Group LASSO (SGL) in identifying factors influencing DHF incidence across Indonesian provinces. Three penalized regression models were applied to data comprising 29 predictors organized into five thematic groups. Model performance was assessed using the coefficient of determination (R²) for data testing, with additional analysis of variable selection patterns to evaluate parsimony and epidemiological interpretability. LASSO was employed to identify dominant individual predictors, Group LASSO to assess the contribution of thematic variable groups, and SGL to simultaneously perform selection at both group and individual levels. The results indicate that SGL provides the best predictive performance, achieving an R² of 94.94%, followed by LASSO (88.91%) and Group LASSO (73.94%). LASSO produced the most parsimonious model, selecting only four socio-economic and demographic variables, but overlooked meaningful group structures. Group LASSO retained three complete groups (Demographic, Socio-Economic, and Sanitation/Residential Environment) but exhibited reduced accuracy due to the inclusion of weakly contributing variables. In contrast, SGL selected 23 relevant variables while preserving group structures, revealing that DHF incidence is primarily associated with socio-economic factors (unemployment, labor force size, poverty), demographic pressure, sanitation and housing conditions, and rainfall. In conclusion, Sparse Group LASSO emerges as the most effective method for identifying significant DHF determinants, offering an optimal balance between predictive accuracy, model parsimony, and epidemiological interpretability. The findings advocate for the adoption of integrated, data-driven regularization approaches in developing targeted DHF prevention and control strategies in Indonesia.
Analyzing comorbidity index for cancer patients during management using semi Markov chain process
Globally, cancer is a high health burden, as it’s the fifth leading cause of death, in the year 2022, a study by the WHO showed there were an estimated 20 million new cases, and the number of deaths totaled 9.7 million. In a group of 5 people, 1 person develops cancer in their lifetime, approximately 1:12 women and 1:9 men die from cancer. The management of cancer patients is complicated by the presence of comorbidities, which can significantly affect transition between time of diagnosis and treatment initiation. The data used in the study was sourced from Cancer regional registry. It entailed the following details of the patients: age, gender, cancer staging, pre-existing and post-existing health condition(s), and cancer subtype. Patients were grouped according to the stage in which they were diagnosed. Stage I and II are the early stages, while Stage III and IV are the late stages. The most prevalent cancer type in males is prostate cancer, while in females it is breast cancer, with hypertension as the commonest comorbid condition. Most of the cancer diagnoses were in late stage, that’s stage III and stage IV. This distribution showed that a significant proportion of patients were diagnosed at advanced stages (III and IV), which could affect treatment options and prognosis. Cardiovascular conditions were associated with cancer progression, treatment method, and overall survival. Patients diagnosed at later stages (III-IV) received systemic therapies as their treatment type. There is need to continue the study in the future by prospective researchers in terms of size and variability of the participants investigated at several centers in order to enhance inclusivity. This would be efficient by the use of longitudinal designs so that it monitors disease progression, its response and also model survival rates too.
Mathematical modeling of filariasis and HIV/AIDS co-infection in the context of vector dynamics
This study develops a unique fractional-order model comprising nine compartments to explore the dynamical behavior of the progression of filariasis and the immunosuppressive retrovirus (HIV) comorbidity under explicit vector influence. The study utilizes fractional order stochastic to examine the dynamics of randomness in the epidemiological model. Using fractional stability theory, we determined the reproduction number, the pathogen-absent state equilibrium, and the requirements for dual-level stability analytically. A center manifold approach is used to investigate bifurcation behavior at the critical threshold, demonstrating the forward bifurcation which the disease transmission can be early controlled by reducing R0 less than zero. A fractional optimal control framework is developed; utilizing Pontryagin’s Maximum Principle in the context of fractional order to provide the best strategy to manage the spread of the co-infection. We then did the numerical simulations to verify the theoretical findings and demonstrated how fractional stochastic models provide better understanding of the fluctuations in epdermiological models, and how fractional order can affect disease persistence and the effectiveness of combination therapies. According to our findings, in areas where HIV/AIDS and filariasis are endemic, memory effects play a crucial role in co-infection dynamics and provide guidance for developing integrated, economically sound public health programs. We also found out that, the strategic implementation of intense initial interventions is crucial in managing filariasis and HIV/AIDS co-infection. Thus, by prioritizing early intervention measures, the overall burden of the diseases would be significantly minimized. Therefore, the insights from our study will inform targeted interventions, optimize resource allocation, and improve the overall efficacy of public health strategies in regions burdened by multiple endemic diseases.
Skeletal keypoint-based pipeline as a computer vision-based approaches
Classroom engagement analysis plays an important role in understanding students’ learning behaviors in a non-intrusive manner. However, many existing computer vision-based approaches rely on complex deep learning architectures and time-consuming dataset construction, which limit their applicability in practical classroom settings. This study proposes a skeletal keypoint-based pipeline for classroom engagement analysis that combines person detection and single-person pose estimation. The extracted keypoints are represented in a low-dimensional form and used as input features for lightweight engagement classification models. Two classifiers, namely Ridge Classifier and Gradient Boosting, are evaluated to assess the effectiveness of the proposed pipeline. Experimental results on a test set of 432 samples show that the Ridge Classifier achieves an accuracy of 0.90 with a macro-average recall of 0.94. In contrast, Gradient Boosting achieves an accuracy of 0.96 and a macro-average precision of 0.98. In addition, the proposed pipeline significantly improves data collection efficiency, achieving approximately a sixteen-fold reduction in pose data acquisition time compared to conventional approaches. These results demonstrate that the proposed pipeline provides a practical solution for individual-level classroom engagement analysis under controlled classroom.
A study of energies and thermodynamic aspects of polycyclic aromatic compounds in quantitative structure-property relationship
Polycyclic refers to a molecule that contains more than one cyclic component, usually two or more fused rings. Naphthalene, anthracene, pyrene, and benzo(a)pyrene are examples of polycyclic organic compounds. A number of polycyclic hydrocarbons are hazardous to human health, including the carcinogen benzo(a)pyrene found in automobile exhaust and tobacco smoke. The natural world has a wide range of polycyclic aromatic compounds, including pigments from plants and animals, that are notably beautiful and have unusually high chemical stability. In this study, polycyclic aromatic compounds are modeled as molecular graphs using their molecular structures, and various energies are computed for them. There is also statistical modeling for the physicochemical properties of energies. For these substances, the QSPR analysis is performed, and conclusions are based on the results.
Mathematical analysis of malware propagation in computer networks using an SPIQR epidemic model
In modern times, malware poses a significant threat to information security. Consequently, numerous mathematical models have been developed to describe the dissemination of malware. This study presents the SPIQR (susceptible-protected-infectious-quarantined-recovered) model to analyze how malware spreads through computer networks. We initiate our analysis by formulating a nonlinear dynamic equation that delineates the dissemination of malware. Subsequently, we compute the basic reproduction number (BRN), denoted by R0 for the proposed model. The network's stability is contingent upon the magnitude of R0. Particularly, if R0 < 1, the system remains free from malware, while if R0 >1, the network enters an endemic state. We determine the system's equilibrium points and perform numerical simulations to investigate malware spread within the network, aiming to confirm our theoretical results. In addition, we examine the nodes' communication range to provide more comprehensive data. We also examine the networks' susceptible, protected, infected, quarantined, and recovered node dynamics.
The influence of non-linear harvesting on a web of two prey with one predator, combined with fear and refuge strategies concerning prey populations
Under the influence of various functional responses: Beddington–DeAngelis and Holling type IV via predator interaction with two different kinds of prey populations in a web containing all these creatures, tends for discussing two distinct types of investigations, analytical and numerical, the proposed model joining two defense strategies concerning the prey populations, fear effects and refuge respectively, with presentence of the force of nonlinear harvesting act as a pressure on both prey populations, the system’s positivity and boundedness is the analytical already start with, seven acceptable equilibrium points find the existence, then the Jacobian and Lyapunov methods concerning these equilibria utilize to understand local and global stability conditions. Confirming analytical investigations via numerical analysis simulations, where the analytical results assert that fear and refuge stabilize population dynamics and reinforce coexistence. Still, while acting, the nonlinear harvesting can play a crucial role in determining the system's coexistence, extinction, or biodiversity loss. Sustaining predator–prey ecosystems confirms the Importance of Indirect strategies, as fear and refuge, and harvesting.
Stability analysis and optimal control of a mathematical model for heroin use dynamics
The widespread abuse of heroin, a potent opioid derived from morphine, continues to pose serious challenges to public health due to its high addiction potential and neurotoxic effects. In response to the growing demand for evidence-based intervention strategies, this study develops a compartmental mathematical model to better understand the transmission dynamics of heroin dependence. The model incorporates a distinct compartment for recovering individuals, explicitly accounting for the risk of relapse. Analytical investigation yields the equilibrium states of the system, and the basic reproduction number R0 is derived to characterize the threshold behavior. Stability analyses -both local and global- are conducted for the heroin-free and endemic equilibria using the Routh-Hurwitz criterion and Lyapunov functionals. A sensitivity analysis is performed to identify key parameters that influence the persistence of addiction. To evaluate possible interventions, we introduce optimal control strategies that emphasize awareness programs and nonpharmaceutical approaches. Numerical simulations carried out using MATLAB support the theoretical results and highlight the potential effectiveness of combined strategies in curbing the spread of heroin use. These findings provide a theoretical foundation for informed public health decision-making in the context of opioid addiction.
The role of media coverage in the dynamics of the spread of drug addiction
A mathematical epidemiological model is developed to investigate the influences of media and assess its efficacy in reducing and declining drug use among new users, whether as a direct or indirect beneficial influence. A nonlinear differential equations system serves as the foundation for this model. Numerical simulations and mathematical analytic techniques are used to study the dynamic behavior of the suggested model. Under certain restrictions, the local and global stability of the equilibrium points is analytically asymptotically stable. Additionally, the model exhibits a forward bifurcation, which was examined using central manifold theory, demonstrating notable advancements in the fight against drug use in society resulting from media influence. To validate these analytical results, numerical simulations were carried out using MATLAB version R2021a code. The findings show which parameters should be improved and which should be targeted.
Fusion of EfficientNet‑B0 and MobileNetV2 for tomato disease classification
The study proposes a fusion architecture for tomato disease classification that combines two complementary backbones—EfficientNet‑B0 and MobileNetV2—via feature concatenation and a hierarchical fusion head. Each branch applies global pooling, dropout, and fully connected layers before feature aggregation; the concatenated representation is then processed by the fusion head to produce the final prediction. Evaluation on an independent test set (n = 1000) shows that the fusion model reduces loss and improves overall accuracy relative to individual backbones, effectively correcting many systematic errors made by MobileNetV2 while achieving performance comparable to EfficientNet‑B0. Paired statistical testing using McNemar's test indicates the reduction in misclassifications is practically significant, suggesting the improvements are not attributable to random variation. The concatenation-based aggregation preserves full channel-wise information from both backbones, enabling the fusion head to learn cross-channel interactions and selectively reweight complementary signals to mitigate the weaknesses of each backbone. These findings support the use of feature‑level fusion to enhance the robustness and accuracy of plant disease classification systems.
Comparison of geographically weighted and mixed geographically weighted negative binomial regression models for tuberculosis incidence
Tuberculosis (TB) remains a major public health concern characterized by spatial variation and overdispersion in case counts. In small-area analyses, differences in local characteristics may generate spatial heterogeneity in the relationships between risk factors and TB cases. Conventional Poisson regression is often inadequate because the equidispersion assumption is frequently violated and the model does not account for spatially varying effects. This study aims to model the number of TB cases in Makassar City in 2024 across 15 districts by comparing Geographically Weighted Negative Binomial Regression (GWNBR) and Mixed Geographically Weighted Negative Binomial Regression (MGWNBR). Seven predictor variables were examined: population density, health workforce, health facility, Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), access to safe water, clean and healthy behavior practices (CHB), and malnutrition prevalence. Diagnostic tests indicated the presence of overdispersion and spatial heterogeneity, thereby justifying the application of spatial count regression models. Model comparison based on deviance and the Akaike Information Criterion (AIC) demonstrated that MGWNBR provided a superior fit. The CHB variable exhibited a global effect, whereas the remaining predictors showed spatially varying effects. These findings suggest that determinants of TB cases are not spatially homogeneous, and that a mixed modeling framework more effectively captures local dynamics while maintaining model parsimony. The results support the development of geographically targeted TB control strategies in small-area settings.
An optimal eighth order Ostrowski's - based iterative scheme for nonlinear systems arising in biological and neuroscience models
This paper presents an optimal eighth order multi-step iterative method based on Ostrowski’s fourth-order approach, designed for solving nonlinear equations and systems with high efficiency and accelerated convergence. The method requires only three function evaluations and one derivative evaluation per iteration, achieving high precision while reducing computational costs. It satisfies the Kung–Traub optimality conjecture for n-4, ensuring theoretically optimal convergence. This method is particularly suitable for applications in mathematical biology and neuroscience, such as modeling interacting with neuronal populations, gene regulatory networks, and multi-compartment physiological systems. Numerical examples demonstrate that the proposed approach provides fast, accurate, and reliable solutions, outperforming classical methods and offering a versatile tool for analyzing complex biological and neural systems.
A dynamic compartmental model for optimizing leak detection in water distribution systems
Water distribution systems face significant challenges from leaks that lead to substantial water losses and increased maintenance costs. Traditional detection methods often fall short in addressing the dynamic nature of pipe degradation and leak propagation. This paper introduces a novel compartmental approach inspired by epidemiological models, categorizing pipes into three states: intact, leaking, and repaired. We analyze equilibrium points and stability to identify critical thresholds for leak control and propose optimal strategies for preventive and repair efforts. Numerical simulations validate the theoretical findings, demonstrating the model’s effectiveness in guiding maintenance decisions. This innovative methodology offers a new framework for optimizing resource allocation and improving the resilience of water distribution systems.
Analytical and numerical investigation of influenza transmission with vaccination and treatment: stability, sensitivity and bifurcation perspective
The impact of parameters on influenza dynamics deeper insights to understand and control the spread of the disease. The present study examines the impact of parameters on the transmission dynamics of the influenza virus through a nonlinear SVITR (Susceptible–Vaccinated–Infected–Treated–Recovered) model. The Basic Reproduction Number R0 is studied using Next Generation Metrix. Furthermore, the model exhibits transcritical bifurcation, where stability shifts between equilibria as critical parameter thresholds, particularly the transmission rate, are crossed. Numerical simulations validate the theoretical results, demonstrating the sensitivity of disease dynamics to variations in the model and identifying key parameters that significantly influence disease spread. Based on different parameter values, stability analysis is performed for disease-free and endemic equilibria. The model's positivity, boundedness, and uniqueness are also established to ensure biological feasibility. These findings offer valuable insights for designing effective control strategies against influenza outbreaks.
Confidence interval estimation in a multipredictor spline quadratic regression model for modeling hba1c levels in diabetes mellitus cases
Modeling HbA1c levels in patients with diabetes mellitus is commonly conducted using parametric regression; however, this approach is often inadequate in capturing nonlinear relationships with metabolic predictors. This study aims to develop a multipredictor quadratic spline regression model with confidence interval estimation to model HbA1c levels flexibly. The model incorporates five predictor variables: body weight, fasting blood glucose, HDL cholesterol, LDL cholesterol, and triglycerides, and is implemented using RStudio version 2024.12.0. The results clearly demonstrate nonlinear relationships between HbA1c levels and all predictors. LDL cholesterol shows the strongest influence. Threshold effects are observed for body weight and HDL cholesterol, while glucose and triglycerides exhibit moderate nonlinear patterns. The visualization of the fitted curves and confidence bands supports a more interpretable representation of the model. Overall, the quadratic spline regression with confidence intervals provides a flexible and informative framework for modeling HbA1c levels, particularly when nonlinear associations are present.
Nonstandard finite difference schemes for solving systems of two linear fractional differential equations with complex eigenvalues
This paper presents a novel nonstandard finite difference (NSFD) scheme for solving a system of linear Caputo-type fractional differential equations (FDEs) characterized by complex eigenvalues. First, the analytical solutions for a two-dimensional linear FDE system are derived. The proposed numerical method is then constructed by employing complex Mittag-Leffler functions for establishing numerator and denominator functions within the NSFD framework. A rigorous convergence analysis is conducted, establishing the scheme’s consistency and stability. It is proven that the method is unconditionally stable provided the real parts of the system’s eigenvalues are negative. The applicability of the scheme is extended to fractional harmonic oscillators by reformulating them as equivalent systems of FDEs with complex eigenvalues. The efficacy and accuracy of the method are demonstrated through four numerical examples. The solutions exhibit the expected damped oscillatory behavior. The results show exceptional accuracy, with errors on the order of 10-15 or less, even when using relatively large step sizes, demonstrating the robustness and computational efficiency of the proposed scheme.
Hybrid ensemble learning model with bald eagle search feature optimization for heart disease diagnosis
Heart disease is a wide phrase that covers a variety of disorders affecting the heart and blood arteries. It’s one of the leading causes of death globally. In some cases, heart disease develops silently over years, showing no symptoms until a major event like heart attack or stroke occurs. It results from a complex interplay of lifestyle, genetic, metabolic, and environmental factors. The growing prevalence and complexity of heart disease highlight the urgent need for intelligent, data driven early prediction systems. Healthcare, which has been transformed by machine learning, a branch of Artificial Intelligence, allows computers to learn and make precise predictions. Unlike traditional statistical techniques, Machine learning algorithms can model complex, non-linear relationships and capture subtle patterns in medical data, making them highly suitable for heart disease prediction. These models exposed to more data, so they improve over time, this makes them more accurate with continuous retaining. This work uses Bald Eagle Search optimization, a metaheuristic algorithm for feature selection. For classification, ensemble methods such as Random Forest, Voting, Bagging, Stacking, and various boosting techniques are used. The dataset for this work has 1025 instances and 14 features which is obtained from Kaggle. The result shows that Stacking has achieved highest accuracy of 95.61%.
A probabilistic and graph-theoretic approach to the analysis of credit and liquidity risk contagion with delay effects in a banking system
This paper proposes a probabilistic interpretation of a delayed banking contagion model driven by the interaction between credit risk and liquidity risk. Instead of restricting the analysis to a local study around an equilibrium, the banking system is represented by a weighted directed graph whose vertices are banks and whose edges describe bilateral exposures. This framework captures bank heterogeneity, exposure intensity, network structure, and delayed transmission effects in a unified way. Each institution evolves among four financial states: healthy, exposed to credit risk, exposed to liquidity risk, and defaulted. Transitions between these states are described by probabilities depending on neighborhood conditions, exposure weights, and delay parameters. We further introduce structural indicators, including centrality measures and a synthetic systemic vulnerability index, to identify the institutions most likely to amplify contagion. Numerical simulations show how small changes in transmission and recovery mechanisms may shift the system from a contained stress regime to a generalized crisis.
On topological characterization of hierarchical hypercube interconnection networks using M-polynomials
The application of the M-polynomial to chemical networks, particularly to chemical compounds, is a relatively recent development in chemical graph theory. Despite its novelty, this approach has proven to be a powerful and effective tool for deriving degree-based topological indices. These indices play a crucial role in modeling and predicting various physicochemical properties as well as biological activities of chemical substances and nanostructured systems. The M-polynomial offers a unified and systematic framework by establishing mathematical relationships between molecular structure and chemical behavior. In this work, we focus on computing the general form of M-polynomials for Hierarchical Hypercube Networks (HHNs). The selected HHNs exhibit a high degree of structural symmetry, which significantly simplifies the analytical computations and enables the exact determination of the corresponding M-polynomials. By exploiting these symmetries, we derive closed-form expressions that describe the underlying degree distributions of the considered nanostructures. Furthermore, several important degree-based topological indices are obtained from the derived M-polynomials. These indices provide valuable insights into the structural complexity and potential chemical dynamics of the studied Hierarchical Hypercube Networks. Finally, the M-polynomials are presented graphically to highlight and visualize the structural characteristics of the HHNs.
A hybrid multi-GARCH transformer model for stock price volatility forecasting
Accurate forecasting of financial market volatility remains a central challenge due to nonlinear dynamics, volatility clustering, and regime-dependent behavior in financial time series. While econometric volatility models capture conditional heteroskedasticity, deep learning architectures provide flexible nonlinear modeling capabilities. However, existing approaches often treat these paradigms independently or combine them post hoc, limiting their ability to jointly exploit statistical structure and representation learning. This study addresses this gap by proposing a hybrid architecture that integrates GARCH-family models directly within an attention-based Transformer through representation-level learning. We develop a Multi-GARCH Transformer architecture that embeds conditional variance estimates from multiple GARCH-family models into the input representation of a Transformer encoder. This hybrid architecture functions as a heterogeneous ensemble in which complementary volatility dynamics are integrated at the feature level and adaptively weighted through self-attention. The analysis uses daily stock returns of Safaricom from 2015 to 2024.The study combines descriptive statistics, econometric modeling, and transformer-based hybrid forecasting. Model performance is evaluated using quasi-likelihood (QLIKE), RMSE, and MAE, alongside statistical comparison using the Diebold-Mariano test. Empirical findings confirm that the Multi-GARCH-Transformer ensemble achieves superior out-of-sample volatility forecasting performance by embedding complementary econometric signals within an attention-based architecture. This hybrid model delivers the strongest performance under QLIKE and exhibits statistically significant improvements validated through Diebold-Mariano testing. The observed training behavior indicates more stable convergence and stronger generalization than non-hybrid architectures, reinforcing the effectiveness of representation-level ensemble learning. The results highlight the advantages of integrating econometric theory with attention-based deep learning through feature-level hybridization. By jointly modeling structured volatility dynamics and nonlinear temporal dependencies, the approach improves forecasting robustness, interpretability, and predictive accuracy. This study contributes to the intersection of financial econometrics and deep learning by introducing a hybrid ensemble modeling strategy for complex and evolving market environments.
Semi-analytical treatment of fuzzy risk diabetes model in Oman
Diabetes mellitus is a growing public health concern in Oman, with rising prevalence linked to lifestyle changes and genetic factors. Effective risk prediction models are essential for early intervention and management. In the interim, the semi-analytical approaches may offer simpler solutions without requiring large-scale numerical calculations or linearization and discretization techniques which could be difficult to use with fuzzy differential equation models. This study proposes a semi-analytical approach to develop a robust diabetes risk model tailored to the Omani population. By integrating clinical, demographic, and biochemical variables, the model employs a combination of analytical techniques to enhance predictive accuracy while maintaining interpretability. Additionally, solving the fuzzy differential equations, a semi-analytical method is employed, combining analytical techniques with numerical approximations to handle the inherent fuzziness in the system. This approach ensures robust and interpretable solutions while accounting for imprecise data common in medical studies. The model is validated using real-world clinical data from Oman, demonstrating its effectiveness in predicting diabetes risk trends under different scenarios. The proposed solution offers a scalable tool for policymakers and healthcare providers to implement targeted prevention strategies, potentially reducing the diabetes burden in Oman. The results highlight the flexibility of the FDE-based model in addressing uncertainty, providing healthcare policymakers with a valuable tool for early intervention strategies.
An explainable knowledge-driven hybrid CNN-transformer and radiomics framework for multi-modal MRI-based brain tumor analysis
The increasing complexity of medical data in modern healthcare environments necessitates intelligent frameworks that not only deliver accurate predictions but also support effective knowledge generation and decision-making. In the context of brain tumor analysis using multi-modal magnetic resonance imaging (MRI), challenges such as tumor heterogeneity, variability in imaging protocols, and limited interpretability of deep learning models hinder their integration into clinical knowledge workflows. This study proposes an explainable, knowledge-driven hybrid CNN–Transformer–Radiomics (HCTR) framework designed to facilitate both predictive performance and clinical knowledge extraction. The framework integrates convolutional neural networks for localized feature learning, transformer-based architectures for global contextual understanding, and radiomic descriptors for structured, domain-relevant feature representation. A cross-attention-based fusion mechanism is employed to combine these heterogeneous knowledge sources into a unified representation. Beyond detection and analysis, the proposed system emphasizes interpretability through Grad-CAM visualizations and feature attribution methods, enabling the transformation of model outputs into clinically meaningful insights. This supports enhanced transparency, trust, and knowledge dissemination within clinical decision-making processes. The proposed framework contributes to the development of intelligent decision support systems by bridging data-driven modeling with knowledge-centric interpretation. It provides a scalable approach for integrating explainable AI into healthcare knowledge management environments, facilitating improved diagnostic reasoning and informed clinical decisions.
Numerical simulation of two-dimensional pollutant distribution using the Crank-Nicolson method in waste equalization ponds
Domestic wastewater flowing from residential areas to wastewater stabilization ponds generally contains various pollutants. In this study, research was conducted to investigate the distribution of pollutants in wastewater ponds by observing changes in biochemical oxygen demand (BOD) parameters. The concentration of pollutant tested was same as the concentration of BOD. The phenomenon of waste particle distribution in domestic wastewater stabilization ponds is viewed as an advection-diffusion scheme. The advection-diffusion scheme can be developed into a mathematical model, specifically a two-dimensional partial differential equation. In this study, numerical methods were used to solve these equations. The Crank-Nicolson method was used to discretize partial differential equations in time and space. The purpose of this study was to determine the points of pollutant dispersion in liquid wastewater stabilization ponds. The dispersion of wastewater pollutants is displayed in simulations performed using Python 3.14.0. The stability analysis of the Crank-Nicolson method is investigated using the Von Neumann method. The results of the analysis show that the Crank-Nicolson method is convergent and unconditionally stable.
Outlier detection and control in Bayesian vector autoregressive processes
While outlier detection and control are not fundamental to the estimation of Bayesian Vector Autoregressive (BVAR) models, they represent a significant improvement that may enhance the robustness and reliability of estimation and forecasting. The increasing prevalence of big data pose challenges for monitoring and maintaining data quality for estimation. In this paper, the standard BVAR model is extended through Outlier Detection and Control (ODC), known as Extended BVAR-ODC model. The Extended BVAR-ODC decomposes the data generating process into three explicit components: a core VAR process with coefficient matrices and Gaussian innovations, an outlier component, and a multivariate normal-tempered innovation structure. Also, the ODC mechanism operates through a two-stage Bayesian procedure: posterior inference on outlier indicators, and simultaneous estimation of outlier impact coefficients and VAR parameters. It is shown in simulation that the Extended BVAR-ODC provides superior fit compared to the standard BVAR. Across all diagnostics, including parameter recovery, posterior trace convergence, mixing and predictive calibration, the Extended BVAR-ODC consistently outperforms the standard BVAR. Particularly, the posterior estimates of intercepts, lagged coefficients, and covariance matrices exhibit higher stability and smaller posterior dispersion. Additionally, residual analyses and goodness-of-fit confirm that the Extended BVAR-ODC effectively mitigates distortions induced by outliers. All performance measures favour the Extended BVAR-ODC, highlighting its superior generalization performance. Therefore, the Extended BVAR-ODC would be very useful in modeling financial systems that are subject to structural breaks or extreme events. Its outlier-induced specification ensures more robust inference under contaminated data distributions.
Combat harmonization of rs-fMRI improves classifier performance in patients with autism spectrum disorder: a graph theoretical approach
Resting state functional connectivity is identified to reflect the intrinsic organization of the brain’s cognitive characteristics. One key property of such biological networks is modularity. In this paper, an open dataset ABIDE of which a total of 45 ASD (Autism Spectrum Disorder) and 53 TD (Typical Development) participants from KKI and PITT sites was considered. Graph Theoretic measures involving Degree Centrality, Clustering Coefficient and Modularity were calculated at different thresholds of network connectivity. Linear SVM classifiers were used to distinguish between ASD and TD. The classifier was applied to each of these graph measures at individual threshold levels. Further, ComBat harmonization across both sites was performed to which the above classification model was applied. Experimental results show that participants from KKI sites showed a difference in Newman modularity at 0.3 threshold with 76.1% accuracy with specificity and sensitivity of 73.08% and 81.25% respectively. Although the accuracy of the site-specific TD vs ASD classifiers were comparable, the sensitivity of the KKI classifier was less, which improved post harmonization. Similar performance was observed in degree centrality and clustering coefficient measures. Therefore, the harmonization process plays a significant role in classifier performance and in model selection.
Modeling and control of crime using optimal control theory
This research introduces a compartmental mathematical model, inspired by epidemiology, to analyze crime dynamics and design reduction strategies. The population is divided into four groups: susceptible individuals, active criminals, prisoners, and rehabilitated individuals. Two control functions are incorporated: one aimed at deterrence through sanction policies, and the other focused on social reintegration via educational, psychological, and professional support programs. Based on optimal control theory, the model identifies relevant intervention strategies to prevent crime and improve its management. The theoretical findings are validated through numerical simulations using MATLAB, comparing the system’s evolution with and without intervention. This approach offers a solid analytical framework to support the development of integrated, flexible, and adaptive public policies addressing contemporary crime-related challenges.
A stage-structured diabetes progression model with control-relapse dynamics: well-posedness, persistence, and stability
We develop and analyze a nonlinear stage-structured model for diabetes progression that distinguishes uncontrolled and controlled diabetes and incorporates bidirectional management–relapse transitions together with bounded prevalence-weighted deterioration effects. We establish local and global well-posedness, positivity, boundedness, and the existence of a positively invariant feasible region. We also characterize the equilibrium structure, derive conditions for the existence of a positive equilibrium, and prove persistence of the downstream disease classes. A central analytical feature is the decomposition of the system into an upstream linear subsystem and a downstream nonlinear subsystem, which clarifies the mechanism governing the long-term dynamics. Local asymptotic stability of the positive equilibrium is established through Jacobian analysis and the Routh–Hurwitz criterion, while a Lyapunov–LaSalle argument yields a sufficient condition for global asymptotic stability. As an extension, we formulate an optimal-control version of the model with prevention, management, and relapse-prevention interventions and solve the resulting system numerically over a 10-year horizon. The simulations show that combining all three controls yields the largest reduction in uncontrolled diabetes and severe-complication burden, while sensitivity analysis identifies the progression-related parameters with the strongest influence on severe outcomes. These results show that the proposed framework is mathematically tractable and biologically relevant for studying long-term diabetes complication dynamics.
A non-stationary NB–INAR(1) model with time-varying beta–binomial thinning: properties and rolling estimation
Many count time series exhibit non-stationary behavior such as drifting intensity, regime switching, structural breaks, evolving serial dependence, and changing innovation levels, which can invalidate classical stationary INAR models. We propose a non-stationary Negative Binomial INAR(1) model based on Beta–Binomial thinning, where the thinning variable is randomized through a Beta mixing distribution. This construction yields a transparent decomposition of dynamic persistence and dispersion: the time-varying mean persistence \(\tau_t=\mathbb{E}(\Theta_t)\) governs dependence, while the time-varying precision \(\phi_t=a_t+b_t\) controls survivor-variance inflation induced by mixing. We derive closed-form conditional moment identities and time-varying recursions for the unconditional mean, variance, and autocovariance structure, highlighting how dependence propagates through products of \(\tau_t\). For inference, we develop practical moment/CLS-based procedures under local stationarity, including rolling-window CLS and smooth parametric evolution for \((\tau_t,m_t)\), together with feasibility constraints that preserve the probabilistic interpretation. Monte Carlo experiments under multiple non-stationary regimes demonstrate accurate tracking under gradual evolution, quantify the RMSE–adaptation-delay trade-off induced by window length under breaks and regime switching, and illustrate the volatility impact of precision variation. An application to weekly accident counts shows that a smooth time-varying innovation mean improves one-step-ahead forecasting, residual lag dependence becomes negligible after mean correction, and negative binomial predictive intervals provide improved coverage under mild overdispersion.
Bayesian Spatio-Temporal Model Simulation of Climatic Influences on Malaria-Typhoid Dynamics
Typhoid and Malaria are among the world’s biggest health challenges, with the Sub-Saharan Africa being the most affected. Although typhoid and malaria have different causes and modes of spread, they both have similar symptoms. Despite historical attempts to model the co-occurrence of malaria and typhoid have achieved some success, they often overlooked the inter-connectedness between climatic factors, spatio-temporal disease transmission dynamics, and co-infection. The aim of this study was to develop a Bayesian hierarchical spatio-temporal model for the joint analysis of malaria and typhoid, which includes both specific and shared spatio-temporal effects under an influence of climatic factors. Parameters for variables such as temperature, precipitation and humidity were estimated under a simulation study using Bayesian inference through the R-INLA approach which serves as an alternative to MCMC since it tackles even intractable integrals. After developing the model, we assessed the properties of the estimators. Plots alongside statistics such as RMSE, variance, Shapiro, mean Z, Skewness, Kurtosis among others were done. The estimator was unbiased, asymptotically normal, asymptotically efficiency and consistent. For increasing observations beyond 1,000 the bias, standard deviation and root mean squared errors (RMSE) decrease hence the sample size was sufficient. Thanks to the simulations, it was observed that temperature had a high impact on the co-infection than other variables while lower precipitation was associated with increased risk. The structured spatial effect revealed a bigger influence on the disease co-infection than other spatial and temporal components.
Estimation of geographically weighted panel nonparametric regression models with spline estimators
Geographical regression modeling approaches have developed rapidly in addressing spatial variation in data analysis. Geographically Weighted Regression (GWR) and Geographically Weighted Panel Regression (GWPR) models are widely used to accommodate spatial heterogeneity, but they still have limitations in capturing nonlinear relationships between variables. Therefore, this study proposes a Geographically Weighted Panel Nonparametric Regression approach with a truncated spline estimator (GWPRS) as an extension of the GWPR model. This model integrates nonparametric regression with spatial and temporal weighting in panel data. The selection of optimal knot points is performed using Generalized Cross Validation (GCV). The case study was applied to Human Development Index (HDI) data in South Sulawesi Province for the period 2019–2023 with a Fixed Gaussian Kernel weighting function and a Fixed Effect Model approach. The results show that the optimal model is obtained at order m=1 with two knot points, and the independent variables that affect HDI differ between districts/cities, forming seven regional groups based on significant variables.
A multi-age mathematical model of psychological stress, HbA1c dynamics, and metabolic disorders with control strategies
This article investigates how psychological stress-social, economic, and intellectual-affects HbA1c dynamics and related health outcomes across different age groups. The population is divided into six compartments: healthy, stressed, elevated HbA1c, prediabetic, diabetic, and individuals with cardiovascular complications. Age-specific analysis includes young adults (18–25), middle-aged adults (45–66), and older adults (\(>70\)), emphasizing differences in stress response and metabolic regulation. Two intervention strategies are proposed: \(u(t)\), targeting stress reduction through family support and yoga practice, and \(v(t)\), promoting healthy diet and lifestyle habits. Numerical simulations demonstrate that combining both strategies effectively lowers HbA1c levels, slows diabetes progression, and reduces cardiovascular risks across all age groups. The optimal control problem is formulated to minimize the number of stressed and metabolically dysregulated individuals as well as the associated intervention costs, and Pontryagin’s Maximum Principle is applied to determine the optimal strategies.
A novel unit Garima-generalized extreme value distribution for stationary and non-stationary modeling: Application to PM2.5 maxima in Thailand
This presented an extended generalized extreme value (GEV) distribution called the unit Garima-generalized extreme value (UGa-GEV) model for block maxima (BM) analysis. Its properties, including survival, hazard, quantile, and return level functions, are proposed. The parameters of the proposed models were estimated by the maximum likelihood method, which was applied to analyze the maximum monthly particulate matter with a diameter of less than 2.5 microns (PM2.5) in Thailand. Climate change has undermined the reliability of stationary models to accurately record extreme environmental events over the last few decades with exploration now focusing on nonstationary factors like trends and shifts to better reflect changing environmental conditions. To address this problem, this study proposed non-stationary maxima models using covariates in meteorology (PM10, ozone, carbon monoxide, nitrogen dioxide, and sulfur dioxide) and time. The optimal model between the GEV and UGa-GEV was compared using real data analysis in Bangkok and Chiang Rai, Thailand with our results demonstrating that the proposed UGa-GEV model provided more flexibility than the traditional GEV model.
Dry bean seed classification using data preprocessing techniques and distance-based classifiers
The development of automatic systems for agricultural applications, particularly for seed classification, is a task of paramount importance to the agro-industrial sector. Accurate recognition of seed varieties supports varietal purity and scalable processing. As one of the world’s most significant crops, dry beans underscore the essential need for precise seed classification to safeguard crop quality and enhance agricultural productivity. Traditional methods for dry bean seed classification involve manual and semiautomated approaches, that are inadequate for scale processing, suffering from slow throughput and reliance on human interpretation. In this work, we proposed a pipeline that is centered on Weighted K-Nearest Neighbors (WKNN) for classifying seven dry-bean varieties. Firstly, we remove outliers per class and features via the IQR rule, rebalance classes, and standardize features with Z-score normalization. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for the classification of 7 different dry bean seeds in comparison with other classical machine learning and state-of-the-art approaches.
Imperfect delayed recovery \(\delta\)-shock model under a mean geometric process
A biological reliability model is presented that integrates \(\delta\)-shock failure, deteriorating tolerance, delayed imperfect recovery, and mean-geometric-process recovery deterioration. The model identifies the optimal number of failure-recovery cycles before system renewal by minimizing the long-run average biological burden. A control-limit theorem is established to characterize the optimal replacement threshold, with numerical validation confirming the result. Sensitivity analysis quantifies how tolerance deterioration rate, recovery efficiency, and delay probability influence optimal renewal timing. This framework yields quantitative insight into life-history trade-offs in stress-prone biological systems and provides a rigorous mathematical basis for assessing recovery-replacement decisions under progressive deterioration.
The fear and toxin effect on Sokol-Howell prey-predator model involving hunting cooperation with anti-predator behaviour and disease in top predator
This study examines the complex dynamic behavior of a multi-trophic epidemiological ecology model, encompassing prey, predators, susceptible and infected apex predators. When a predator consumes, preys according to Holling type-Ⅳ functional response, while the apex predator utilizes a functional response of the Sokol-Howl type. Among other environmental challenges, the model considers the toxic effect on all species, with toxicity levels decreasing from prey to apex predator. Cooperative hunting among predators and apex predators, as well as prey-predator behavior, enhances the model's dynamics. Additionally, the fear effect addresses the psychological impact, driving prey to fear predators and predators to fear apex predators. We conduct a comprehensive qualitative analysis, including the presence and limitations of solutions, the local and global stability of equilibrium points. Numerical analysis demonstrate how toxicity, fear, and cooperative hunting synergistically influence species survival and extinction, revealing the stability of diseased, three-trophic food webs under environmental stresses.
The prey-carrion-eater system's dynamics in the presence of a subsidy: The role of panic and the group’s hunting
In ecology, the group’s hunting behavior and panic are both significant topics. Therefore, a novel three-dimensional dynamical model that integrates carrion availability, a panic-regulated prey population, and a carrion-eater species that simultaneously exploits both subsidy (or carrion) and live prey is proposed and studied. The model incorporates nonlinear functional responses, panic-driven suppression of prey growth, and cooperative hunting in the carrion-eater. Mathematical analysis establishes positivity and boundedness of all state variables, ensuring ecological feasibility. Equilibrium points are explicitly derived, and their local stability is analyzed using the Jacobian matrix, while global stability is assessed through the construction of appropriately designed Lyapunov functions. The results indicate that increasing panic intensity can suppress prey abundance and stabilize the system. At the same time, cooperative hunting enhances the efficiency of carrion-eaters, potentially leading to persistent cohabitation or oscillatory dynamics depending on parameter regimes. These findings extend classical carcass–prey–carrion-eater frameworks, highlighting the critical role of carrion recycling and behavioral mechanisms in shaping multi-trophic ecological dynamics. The model offers a unified theoretical framework for investigating realistic ecosystems where carrion use, panic effects, and mixed feeding strategies combine.
Mathematical modelling of TB–COVID-19 co-infection with time delay and optimal control analysis
Tuberculosis (TB) and COVID–19 remains a major global public health threat, particularly in low- and middle-income countries where healthcare systems face persistent structural constraints. The co-existence of these two diseases introduces complex clinical and epidemiological challenges, such as delayed diagnosis, delayed treatment initiation and overlapping symptoms, which generally increase the disease burden and mortality risk. In this study, we formulate and analyse a deterministic compartmental model for TB–COVID–19 co-infection incorporating time delays representing diagnostic and treatment lags. The model integrates three public health interventions i.e. physical distancing, vaccination and treatment, formulated as time-dependent control functions. Qualitative analysis establishes positivity and boundedness of the solutions, existence of invariant regions and threshold dynamics governed by reproduction numbers. An optimal control framework is then introduced in our work to reduce the number of infections and also the implementation costs. Using Pontryagin’s Maximum Principle, the corresponding Hamiltonian system and adjoint equations are derived, together with the necessary conditions for optimal controls. To further explore the impact of these interventions, numerical simulations are carried out using delay differential equation techniques combined with a forward–backward sweep method. The results suggests that applying multiple interventions simultaneously can significantly lower the prevalence of co-infection and related fatalities. However, the presence of delays reduces the overall effectiveness of these strategies. The model offers quantitative proof that effective management of TB and COVID-19 co-infection in nations with limited resources requires early diagnosis, quick treatment initiation, and coordinated intervention methods.
SIR model analysis of traffic congestion dynamics in urban areas
In highway traffic flows, where most vehicles pass over flyovers to bypass congestion at the macro level, there is a close relationship with the spread of infectious diseases, which are most commonly modeled using the susceptible-infected-recover(SIR) model. To smoothen traffic flow, a control variable is introduced that will be optimized, in this case, the duration of the green light at an intersection. Stability behavior of equilibrium points, reproduction number \(\mathcal{R}_0\), and its effect on the three compartments. The numerical simulations were verified to confirm the previous results. The behavior of the three compartments with respect to the \(\mathcal{R}_0\) value was obtained. The numerical simulation results also showed the positive impact of the duration of the green light on vehicles affected by traffic congestion.
Caputo fractional-order tumor–immune–checkpoint inhibitor modeling: chaos-enhanced particle swarm optimization, ANFIS-driven adaptation, and large-scale in silico simulation for personalized cancer immunotherapy
The interplay between tumors and the immune system during immunotherapeutic interventions is characterized by highly nonlinear, individualized dynamics that conventional integer-order mathematical representations are inadequate to describe. The present work introduces a Caputo fractional-order three-compartment model coupling tumor cell populations, immune effector cells, and checkpoint inhibitor drug concentrations, whose parameters are identified through a chaos-augmented particle swarm optimization (CE-PSO) strategy utilizing logistic-map-based stochastic perturbations. An adaptive neuro-fuzzy inference system (ANFIS) component performs online adjustment of both the fractional differentiation order and immune responsiveness, driven by IL-2 and IFN-γ cytokine-inspired signal inputs. Rigorous proof of global asymptotic stability at the tumor-free steady state is provided through the fractional-order Lyapunov direct approach. Broad computational evaluation across a virtual cohort of fifty simulated patients yields RMSE = 0.066, reflecting an 83.2% accuracy gain relative to classical integer-order alternatives, along with a 16% enhancement in tumor suppression under ANFIS-guided individualized dosing. The full simulation pipeline—encompassing the Adams-Bashforth-Moulton numerical integrator and both the CE-PSO and ANFIS components—is structured for maximal reproducibility. To the authors’ knowledge, this represents the inaugural stability-certified platform to concurrently unify cytokine-responsive real-time ANFIS tuning, CE-PSO parameter optimization, and Caputo fractional-order system dynamics within a single computational environment for patient-tailored cancer immunotherapy.
Optimizing multivariable kernel regression with GCV and UBR for systolic blood pressure modeling in type 2 diabetes mellitus
Random blood glucose and age can influence the increase in systolic blood pressure in patients with type 2 diabetes mellitus. The data pattern forms a random pattern, so the modeling uses multivariable kernel nonparametric regression. This study uses the Nadaraya-Watson estimator because the calculation is simpler and uses the Gaussian kernel function with bandwidth optimization methods are Generalized Cross-Validation (GCV) and Unbiased Risk (UBR). The model with GCV optimization obtained an \(R^2\) of 99.9713%, while the model with UBR optimization obtained an \(R^2\) of 99.9726%, so the model produced by UBR optimization was the best. The model with UBR optimization revealed that systolic blood pressure in patients with type 2 diabetes mellitus was strongly influenced by random blood glucose and age. Evaluation of the UBR optimization model performance on the testing data yielded an Symmetric Mean Absolute Percentage Error (SMAPE) value of 18.05096%. This means that the model performance with UBR optimization has a good ability to predict data.
Modeling of shortwave radiation in Semarang using semiparametric Fourier series regression
This study presents semiparametric Fourier series regression model to analyze shortwave radiation (SWR), photosynthetically active radiation (PAR), and time in Semarang. The semiparametric approach combines the flexibility of nonparametric regression with the structure of parametric models, allowing for better representation of complex seasonal patterns in solar radiation data. The nonparametric component was modeled using a cosine-based Fourier series, which effectively captures the periodic nature of the observations. Data simulation was conducted by generating several training and testing data splits (60:40, 70:30, 80:20, and 90:10) to identify the optimal proportion for model estimation. Based on the Generalized Cross Validation (GCV) criterion, the 90:10 split with one Fourier coefficient produced the most efficient model. Model evaluation using actual SWR and PAR data yielded an in-sample coefficient of determination (R²) of 0.9938, indicating that the model explains nearly all data variability, and an out-sample Mean Absolute Percentage Error (MAPE) of 0.9003%, reflecting very high predictive accuracy. These results demonstrate that the semiparametric Fourier series regression can accurately predict shortwave radiation based on PAR and time, providing valuable insights for agricultural planning and the development of solar radiation–based renewable energy in Semarang.
Role of wind on the dynamic of stage structured predator-prey system with cannibalism
This article presents a new mathematical model of a predator-prey ecosystem that contains biological relations such as wind flow, stage structure, and cannibalism. To perform the dynamic analysis of the system, the positivity and boundedness of the proposed system were examined, and feasible equilibrium points were found. By utilizing the linearization technique and Lyapunov functions, the local and global stability were established, respectively. All conditions for persistence were identified. By the bifurcation theory, the impacts of changing the parameters were examined. A numerical simulation was used to validate our analytical results by using MATLAB code (version R2018b). It is found that the impact of wind flow plays a major role in the dynamics of the system. When the wind flow increases, then the predators (mature and immature) become extinct, which means the effect of wind flow hinders the reduction and efficiency of predators. On the other hand, the cannibalism coefficient also affects the dynamics of the system, whereby the bistable state appears.
Bifurcation analysis and optimal control of an SVITR COVID model with vaccination and treatment strategies
This study proposes and analyzes a deterministic SVITR epidemiological model describing the transmission dynamics of COVID-19 incorporating vaccination and treatment strategies. The total population is divided into susceptible, vaccinated, infected, treated, and recovered compartments. Qualitative analysis of the model is performed to determine the disease-free and endemic equilibria and the basic reproduction number. Local stability of equilibria is examined using Jacobian analysis, while global stability is investigated using Lyapunov techniques. A bifurcation analysis is carried out to examine the qualitative changes in system dynamics near the critical threshold parameter. Sensitivity analysis is performed to identify the most influential parameters affecting disease transmission. Furthermore, an optimal control framework incorporating vaccination and treatment controls is developed using Pontryagin’s Maximum Principle to minimize the number of infected individuals and intervention costs. Numerical simulations illustrate the theoretical findings and demonstrate the effectiveness of combined vaccination and treatment strategies in controlling the spread of COVID-19. The results highlight the importance of timely vaccination campaigns and effective treatment policies in mitigating epidemic outbreaks.
Adversarially robust brain tumor detection in multi-modal MRI using hybrid deep learning and radiomic features
Deep learning has significantly improved automated brain tumor detection from multi-modal magnetic resonance imaging (MRI). However, many existing models remain vulnerable to adversarial perturbations, noise, and domain shifts, which can reduce diagnostic reliability in real-world clinical environments. To address these challenges, this study proposes HCTR-R, a robustness-oriented hybrid computational intelligence framework that integrates convolutional neural networks, transformer-based contextual modeling, and radiomic feature representations for reliable tumor detection. The framework incorporates defense-aware learning strategies, including noise injection, adversarial sample generation using the Fast Gradient Sign Method (FGSM), and adaptive data augmentation to enhance model stability under perturbations. A cross-attention fusion mechanism is employed to combine deep and radiomic features, enabling more effective representation learning from multi-modal MRI data. Unlike conventional hybrid architectures, the proposed approach explicitly focuses on robustness and cross-dataset generalization in medical imaging. Experimental evaluation was conducted using the combined BraTS 2019–2021 datasets for training and internal validation, while the TCGA-GBM dataset was used for external validation to assess generalization across institutions. The results indicate that the proposed framework achieves strong and consistent performance under noisy and adversarial conditions, with high accuracy, F1-score, and AUC values. Explainability analysis using Grad-CAM and SHAP further demonstrates meaningful correspondence between model predictions and tumor regions. The proposed approach supports the development of robust and interpretable computational intelligence systems for AI-driven clinical decision support in healthcare.
Adaptive immune response in HIV dynamics: an optimal control study
This research aims to optimize the control of an HIV infection model. The focus of this paper is on optimizing the control of an HIV infection model. Our mathematical model consists of six non-linear differential equations that illustrate what happens when uninfected cells, exposed cells, actively infected cells, free viruses, and the adaptive immune response interact. Furthermore, adaptive immunity will be characterized by cytotoxic T-lymphocytes (CTLs) and antibodies. The two treatments illustrate how drug therapy can stop the replication of viruses and reduce the risk of new infections. We demonstrate the existence of an optimal control pair and we apply Pontryagin’s minimal approach to delineate these two optimum controls. In the end, numerical simulations are used to show how important effective treatment is for keeping the infection under control.
Stability analysis of delayed co-infection model of pneumonia and meningitis
This study develops and analyzes a delay differential model to investigate the co-infection dynamics of pneumonia and meningitis in a human population. The model incorporates biologically motivated delay factors-such as isolation, social distancing, and public awareness represented through an exponential survival term. These delays capture the influence of behavioral and treatment-related interventions on disease transmission. The model’s dynamical behavior is examined through the analysis of positivity, boundedness, and equilibrium states. Both the disease-free equilibrium (DFE) and disease-existing equilibrium (DEE) are shown to be locally and globally asymptotically stable under appropriate threshold conditions. Sensitivity analysis highlights the influence of key parameters, including delay-related terms, on the basic reproduction numbers of pneumonia, meningitis, and their co-infection subsystem. Numerical simulations support the analytical results and illustrate how increasing delay-based interventions can effectively suppress disease spread. The findings provide valuable insights for public health strategies, suggesting that isolation, social distancing, and awareness campaigns can substantially mitigate co-infection risks in affected communities.
Modeling dependent time to visual acuity recovery using a bivariate copula Cox approach: a study of bilateral cataract surgery
Survival analysis is a statistical framework for modeling time-to-event data. In the context of bilateral diseases such as cataracts, the two eyes may exhibit dependent event times, thereby reducing the suitability of univariate approaches for capturing this dependence. This study aims to model the dependence between the times to achieve a minimum visual acuity of 6/18 in the right and left eyes of patients following cataract surgery using a Cox proportional hazards model with a bivariate copula approach. The data consists of patients who underwent bilateral cataract surgery at an eye hospital in Surabaya, between January 1 and October 31, 2025. Parameter estimation was conducted using a two-stage procedure based on maximum pseudo-likelihood, with numerical optimization performed via the Berndt–Hall–Hall–Hausman (BHHH) algorithm. Four Archimedean copula families (Clayton, Frank, Gumbel, and Joe) were considered to model the dependence structure. Based on the Akaike Information Criterion (AIC), the Joe copula provided the best model fit, yielding an AIC value of 2975.158. The estimated dependence parameter was 3.934, corresponding to Kendall’s tau of 0.609, indicating a moderately strong positive dependence between the times to achieve the minimum visual acuity in both eyes. Additionally, age and the history of cardiovascular disease were found to have significant effects on the event times in both eyes.
Robust Bayesian precision matrix estimation with a regularized horseshoe prior
Estimating high-dimensional precision matrices in Gaussian graphical models is challenging due to sensitivity to outliers and limitations of uniform shrinkage methods. We propose a robust Bayesian approach based on a sparse Cholesky factorization, combining the \(\gamma\)-divergence for outlier resistance with a regularized horseshoe prior to enable adaptive shrinkage. Estimation is performed via MAP optimization using the Adam algorithm, with uncertainty quantified through a Laplace approximation. Theoretical properties including posterior propriety, existence of the MAP estimator, and robustness are established. Simulation results show improved performance in terms of Frobenius norm and root mean squared error under data contamination. An application to gene expression data illustrates the method’s ability to identify potentially meaningful association patterns. The proposed framework provides a computationally tractable approach to robust precision matrix estimation in high-dimensional settings.
Nonlinear dynamics of a predator–prey system with kleptoparasitic interactions
This study explores a predator–prey model featuring one prey and two competing predators linked through kleptoparasitism. Unlike traditional approaches, we introduce a nonlinear formulation where the interaction simultaneously suppresses the host predator’s growth while boosting the kleptoparasite. We determine the system’s equilibrium points and evaluate their stability through the Routh–Hurwitz criteria and Lyapunov methods. Our findings reveal that the system’s dynamics are highly sensitive to parameter shifts. Numerical simulations reveal three main results: full species coexistence, competitive elimination, and bistability. Importantly, the environmental carrying capacity serves as a key factor that initiates changes among these varying dynamic states. Additionally, the presence of bistability highlights that the system’s long-term fate can be completely reliant on the numbers of initial populations. These findings imply that our proposed formulation presents a novel viewpoint compared to traditional interference-based models. By addressing these complex responses, this framework delivers a more refined comprehension of how kleptoparasitic behavior influence predator-prey relationships across various ecological environments.
A Bayesian binary quantile regression approach with adaptive lasso for hypertension modeling: evidence from bootstrap consistency analysis
Hypertension remains one of the most prevalent and serious non-communicable disease worldwide due to its strong association with cardiovascular, and renal complications. This study aimed to develop a robust statistical model to identify significant predictors of hypertension status using the Bayesian Binary Quantile Regression with Adaptive Lasso (BBALQR) method. Given the complexity of health data, BBALQR offers a flexible and robust alternative to traditional models, especially in the presence of outliers and binary responses. The dataset comprises 653 patient records from Arosuka Hospital in Solok, West Sumatra, Indonesia. The proposed model was evaluated across five quantile levels \(\tau\) = 0.05, 0.25, 0.55, 0.75 and, 0.95, and parameter stability was assessed through Bootstrap resampling with 10, 25, and 50 replications. Among the tested quantiles, \(\tau\) = 0.05 yielded the lowest mean squared error (MSE), and greater Accuracy value and Press’s Q, indicating the best model performance. Furthermore, Bootstrap analysis confirmed the consistency and reliability of parameter estimates within the 95% confidence interval. Significant predictors of hypertension include age, body weight, cholesterol, and blood sugar levels. The findings suggest that BBALQR, combined with Bootstrap validation, offers a reliable modeling framework for binary health outcomes with non-normal and heteroscedastic data characteristics.
Cost-sensitive DenseNet-121 with progressive curriculum augmentation and dynamic threshold optimization for imbalanced skin cancer classification
Skin cancer is one of the leading causes of cancer-related mortality worldwide, where early melanoma detection substantially improves patient survival. However, automated dermoscopic image classification remains challenging because malignant lesions are severely underrepresented, causing deep learning models to become biased toward benign cases. This study proposes a clinically oriented deep learning framework for imbalanced skin cancer classification using DenseNet-121 on a rigorously curated subset of the ISIC 2024 dataset. Prior to model development, patient-level integrity validation and duplicate inspection were performed, followed by patient-level data partitioning to eliminate data leakage. A total of 12,000 dermoscopic images were curated and divided into 8,296 training, 1,904 validation, and 1,800 testing images. The proposed framework integrates curriculum-based progressive augmentation, cost-sensitive optimization using weighted binary cross-entropy, MixUp and CutMix regularization, and dynamic threshold optimization to prioritize malignant lesion detection. Experimental results demonstrate that the proposed model achieved a test area under the receiver operating characteristic curve (AUC) of 0.8959 and a malignant recall (sensitivity) of 92.68%, correctly identifying 38 of 41 malignant lesions while missing only three cases. Although the clinically optimized decision threshold reduced precision because of increased false positives, it substantially decreased false negatives, making the framework well suited for preliminary melanoma screening. These findings demonstrate that combining rigorous patient-level curation, progressive augmentation, cost-sensitive learning, and clinically driven threshold optimization provides an effective strategy for improving malignant lesion detection in highly imbalanced dermoscopic datasets.