Statistics, Optimization & Information Computing
Statistics, Optimization and Information Computing (SOIC) is an international refereed journal dedicated to the latest advancement of statistics, optimization and applications in information sciences. Topics of interest are (but not limited to): Statistical theory and applications Statistical computing, Simulation and Monte Carlo methods, Bootstrap, Resampling methods, Spatial Statistics, Survival Analysis, Nonparametric and semiparametric methods, Asymptotics, Bayesian inference and Bayesian optimization Stochastic processes, Probability, Statistics and applications Statistical methods and modeling in life sciences including biomedical sciences, environmental sciences and agriculture Decision Theory, Time series analysis, High-dimensional multivariate integrals, statistical analysis in market, business, finance, insurance, economic and social science, etc Optimization methods and applications Linear and nonlinear optimization Stochastic optimization, Statistical optimization and Markov-chain etc. Game theory, Network optimization and combinatorial optimization Variational analysis, Convex optimization and nonsmooth optimization Global optimization and semidefinite programming Complementarity problems and variational inequalities Optimal control: theory and applications Operations research, Optimization and applications in management science and engineering Information computing and machine intelligence Machine learning, Statistical learning, Deep learning Artificial intelligence, Intelligence computation, Intelligent control and optimization Data mining, Data analysis, Cluster computing, Classification Pattern recognition, Computer vision Compressive sensing and sparse reconstruction Signal and image processing, Medical imaging and analysis, Inverse problem and imaging sciences Genetic algorithm, Natural language processing, Expert systems, Robotics, Information retrieval and computing Numerical analysis and algorithms with applications in computer science and engineering
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Fractional Dynamics and Sensitivity Analysis of a Diphtheria Spread Model
In this paper, we investigate a fractional compartmental model for community-level infectious disease transmission. By employing Caputo fractional derivatives, we formulate an SEIAQR epidemic model. Using fixed-point theory, we establish the existence and uniqueness of solutions for the proposed system. Through a comprehensive mathematical approach, we first establish essential properties of the solutions, including non-negativity and boundedness. The basic reproduction number R0 is evaluated using the next-generation matrix method to determine the potential for disease spread in the community. Stability analysis, we investigate local and global stability existence by applying Matignon’s theorem and constructing an appropriate Lyapunov function. The local stability for disease-free equilibrium point is proved by applying the Routh–Hurwitz criterion. For Sensitivity analysis of the basic reproduction number is conducted, elucidating the most influential model parameters. Finally, a dedicated numerical technique is applied to solve the proposed model. We also present a comparison of the root mean square error (RMSE) for different values of the fractional order α. The results show that α = 0.97 yields a smaller RMSE, suggesting that this value should be considered by the government as a strategy to reduce diphtheria outbreaks and enhance vaccination coverage.
Copyright (c) 2026 Mohamad Tafrikan, Fatmawati, Windarto, Chinwendu E. Madubueze https://creativecommons.org/licenses/by/4.0
Artificial Intelligence in the Production of Multimedia News in Chinese Journalism
The relevance of this research is driven by the rapid implementation of artificial intelligence in the production of multimedia news within Chinese journalism, which is transforming traditional approaches to the creation, distribution, and perception of information. The aim of the study was to investigate the specifics of applying artificial intelligence in the process of creating multimedia news in Chinese journalism and to assess its impact on the organisation of media production and content formation. The research methodology was based on a combination of case analysis, analytical generalisation, systemic, structural-functional, and analytical-prognostic approaches, as well as conceptual modelling, which ensured a comprehensive study of AI integration into multimedia content production in Chinese journalism. Comparative analysis revealed that the level of automation in multimedia content production depends on the material format: textual and audio materials are predominantly semi-automated and require editorial control, whereas video and interactive formats demonstrate a high degree of automation, accelerating production and enhancing audience engagement. Case analysis of the Tencent News, ByteDance, and Xinhua Net platforms identified different strategies for utilising artificial intelligence: Tencent News focuses on efficiency and style standardisation, ByteDance on personalisation and interactivity, and Xinhua Net on content authenticity control and quality. A systemic analysis of the interaction of Natural Language Processing technologies, generative models, computer vision, and artificial intelligence (AI)-powered editing showed that comprehensive integration enables the creation of high-quality multimedia products, scaling of production, and adaptation of content to audience needs. Simultaneously, an analysis of risks and limitations demonstrated that the use of artificial intelligence is accompanied by ethical, social, and security challenges, including algorithmic biases, content generation errors, the formation of “filter bubbles”, and potential information manipulation. The results of the study may be useful to media platforms, journalists, editors, developers of AI technologies for media, as well as researchers in the field of digital communications and media analytics.
Copyright (c) 2026 Aina Duishekeeva, Jiaxing Fei, Lingkun Xue, Shirin Kostiuk, Bingbing Zhao https://creativecommons.org/licenses/by/4.0
Unveiling Quality-Factor Metrics to Optimize Data Collection
Our study introduces a novel quality-factor-driven approach to dataset evaluation, focusing on four key attributes: Class Distribution Index (CDI), Topic Distribution Index (TDI), Average Inverse Document Frequency (IDF), and dataset size. By systematically analyzing these factors, this research assesses their influence on model performance in Natural Language Processing (NLP), particularly in Arabic sentiment analysis. The findings reveal that CDI and TDI exhibit substantial impacts, with CDI showing a strong positive correlation with accuracy (0.5568) and F1-score (-0.7808), indicating that while class distribution imbalance might help the model achieve higher accuracy, it adversely impacts its F1-score, thus reducing the balance between precision and recall. TDI also negatively affects accuracy and F1-score (-0.2242 and 0.2031), underscoring the challenges of uneven text distribution across datasets. In contrast, Average IDF and dataset size positively correlate with model performance, with Average IDF contributing 0.2670 to accuracy and 0.3207 to F1-score, highlighting the predictive power of rare terms within the dataset. Dataset size further enhances F1-score (0.3540), reaffirming that larger datasets support improved sentiment classification accuracy. This study provides foundational insights into the effects of dataset quality on Arabic sentiment analysis, offering strategic directions for future research in underrepresented languages and advancing our understanding of data quality’s implications in NLP.
Copyright (c) 2026 Zouheir Banou, Sanaa Elfilali, El Habib Benlahmar, Fatima-Zahra Alaoui https://creativecommons.org/licenses/by/4.0
Predicting Type 2 Diabetes based on Machine Learning and Deep Learning Models
The global rise of spread the Type 2 Diabetes (T2D) disease has become a major global health challenge. Early detection of this disease is essential for limiting its progression and reducing its potential effect. This study estimates the performance of machine learning (ML) and deep learning (DL) models for prediction T2D using an enhanced version of Pima Indian diabetes dataset. The dataset has been improved through comprehensive preprocessing, feature engineering, and class imbalance handling via Synthetic Minority Oversampling Technique (SMOTE). A total of Seven machine learning classifiers- Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, k-Nearest Neighbors, Naïve Bayes, and XGBoost were assessed alongside three deep learning models Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The Experimental results demonstrate that, XGBoost achieved best predictive performance, with an accuracy (96.84\%, AUC = 0.99), followed by Random Forest (96.32%, AUC = 0.98). Decision Tree and SVM also showed robust performance, while Naïve Bayes was the least accurate 80.5%. In contrast, The DL models achieved an accuracy between (73–77%).
Copyright (c) 2026 Hosam Eldin Fawzan Sayied, Ahmed Abdelhafeez, Mohamed N.M.M. Hassan https://creativecommons.org/licenses/by/4.0
Robust PCA-Based Multivariate Time Series Clustering for Weekly Indonesian Food Commodity Prices with Missing Data
This study proposes an integrated procedure for analyzing multivariate time series (MTS) of strategic food commodity prices across Indonesian regencies/cities, encompassing missing-data imputation, outlier-robust dimensionality reduction, MTS modeling, regional clustering, and cluster-prototype-based forecasting. Weekly price data for eight strategic food commodities across 70 regencies/cities in Indonesia are analyzed through imputation and preprocessing, dimensionality reduction using lag-1 autocovariance-based RPCA (RPCA lag-1), and time series modeling of the lag-1 RPCA principal component scores (RPCA scores) on the first and second principal components (PC1 and PC2) using a first-order vector autoregressive model (VAR(1)) with an intercept term. Regional clustering is performed using non-ensemble methods (K-means Euclidean and PAM Euclidean) to identify the best non-ensemble approach, and Random Forest--General Iterative Clustering (RF-GIC) is also employed as a benchmark. For each clustering result, namely K-means Euclidean as the selected best non-ensemble method and RF-GIC, cluster prototypes are constructed and used for forecasting. The two schemes are compared using a four-fold expanding-window evaluation (root mean square error (RMSE) and mean absolute deviation (MAD)) and a final mean absolute percentage error (MAPE) assessment at 4-step and 26-step horizons, evaluated at both the cluster-prototype and regency/city levels. Based on overall MAPE at the cluster-prototype level, both K-means Euclidean and RF-GIC achieve very accurate performance at the 4-step horizon and accurate performance at the 26-step horizon, with RF-GIC yielding lower overall MAPE than K-means Euclidean at both horizons. At the regency/city level, both methods fall into the accurate category at the 4-step horizon and the moderately accurate category at the 26-step horizon, with only a small difference in overall MAPE (slightly lower for K-means Euclidean). The decrease in accuracy is primarily driven by longer forecasting horizons and commodity-specific volatility, particularly for bird’s-eye chili and red chili.
Copyright (c) 2026 Nina Valentika, I Made Sumertajaya, Aji Hamim Wigena, Farit Mochamad Afendi https://creativecommons.org/licenses/by/4.0
An Explainable Machine Learning–Based Web Tool for Alzheimer’s Disease Diagnosis
Alzheimer’s disease (AD) is one of the most critical global health challenges, and early diagnosis remains essential for slowing disease progression and improving patient management. This study proposes an automated and interpretable machine learning framework for AD diagnosis that combines feature selection, explainable artificial intelligence (XAI), and deep learning approaches to support clinically transparent decision-making. A publicly available AD dataset containing 25 attributes was used. Feature importance was assessed using correlation analysis together with explainability-based importance measures derived from SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), leading to the identification of an optimal subset of features composed of Age, presence of the GeneticRiskFactorAPOE-ε4 allele and FamilyHistoryOfAlzheimer. Several classical machine learning models, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were evaluated alongside Deep Learning (DL) approaches based on Multilayer Perceptron (MLP) architectures. Two MLP-based models were investigated: a feature selection-based MLP (FS-MLP), trained on the selected features, and an encoder-based MLP, integrating deep learning-driven dimensionality reduction. SHAP and LIME were further employed to provide both global and local explanations of model predictions. {The selected model} was deployed in a web-based application enabling AD diagnosis and result interpretation. Performance evaluation using cross-validation, confidence intervals, and statistical significance testing showed that tree-based models achieved the best overall performance, with FS-XGBoost obtaining the highest Area Under the Curve (AUC) of 80.1%, a recall of 77.8% and a specificity of 68.8%, indicating a balanced sensitivity–specificity trade-off for screening applications. To enhance practical applicability, the selected model was integrated into a user-friendly web-based decision-support application for AD diagnosis and interpretation. The results demonstrate that combining feature selection, explainable AI, and machine learning techniques, can provide an effective and transparent framework for supporting early AD diagnosis in clinical settings.
Copyright (c) 2026 Abdelaziz METTAHRI, Sokaina EL KHAMLICHI, M'barek EL HALOUI, Badia ETTAKI https://creativecommons.org/licenses/by/4.0
Determinants of Electronic Examination Success: An Application of Robust Structural Equation Modeling
In this paper, robust procedures for estimating path coefficients in the modeling framework of structural equations are proposed based on robust correlation coefficients. These are two estimates: robust correlation coefficients and Pearson correlation coefficients. The traditional approach performs better than the robust approach using real data of the researcher’s collected through the questionnaire. To find out factors contributing to success in electronic tests, students were sampled using the questionnaire from postgraduate applicants at the University of Mosul. Following the collection of data, 205 valid responses were obtained that contained six independent variables that represented factors which affect electronic tests. The proposed biweight midcorrelation method produced more stable path estimates and improved model fit in the presence of outliers. The results indicate that self-efficacy, quality of use, and user satisfaction are the most influential factors affecting the success of electronic examinations.
Copyright (c) 2026 Salwa Salah Alden Kasim, Mohammed Ameen Oqbah Mohammed, Husam Waleed Yaseen https://creativecommons.org/licenses/by/4.0
Using the First Difference, Forward Orthogonal Deviations, and System GMM Techniques to Measure the Impact of Environmental, Social and Governance Performance on Financial Risks for Companies listed on the S&P EGX/ESG index
This paper proposes the use of the first difference generalized method of moments (FD-GMM),forward orthogonal deviations generalized method of moments (FOD-GMM), and system generalized method of moments (Sys-GMM) techniques to measure the impact of environmental, social, and governance (ESG) performance criteria as independent variables and return on total assets (ROA), leverage (LEV), liquidity (LIQ), firm growth rate (FGR), and size of enterprise (SIZE) as control variables on financial risks as dependent variables, which can be measured by Altman's Z-score index. This study applied to seventeen companies of non-financial institutions which comply with sustainability standards and there are in S \& P EGX / ESG index in Egypt that include various sectors in the period from 2020 to 2024. The study used consistent, valid instruments and coefficient bounds tests to select the best model. The results showed that the two step FOD-GMM model was the most effective model to estimate the relationship between ESG-score, G-score, and control variables on financial risks compared to the FD-GMM and System-GMM models .It was more suitable and safer for small sample that used in this study, in addition they have valid instruments and consistent estimators. It has also a higher predictability compared to other models.
Copyright (c) 2026 Hanaa Hussein Ali https://creativecommons.org/licenses/by/4.0
A Hybrid Machine Learning and Rule-Based Recommendation Framework: A Probabilistic Approach for Reliable Customer Next-Transaction Prediction
This study presents a hybrid recommendation framework that combines machine learning-based probabilistic prediction with rule-based decision logic for forecasting customers' next transactions in digital banking. It aims to help in customer retention and personalization, as well as cross-sell strategies in settings where the behavior of the user is heterogeneous and transaction data are largely imbalanced. The machine learning component estimates behavioral consistency probabilities, whereas the rule-based component translates these probabilities into personalized product recommendations according to customer segmentation and business constraints. The principal methodological contribution is the proposed inverse transform probability mechanism, which reinterprets predicted probabilities as behavioral consistency scores across active, dormant, and new customer segments. This conversion makes it possible to have a uniform probabilistic understanding of the states of the users while maintaining the economic significance of the recommendation decisions. The model performance was evaluated using the area under the precision-recall curve (PR-AUC) because it is a good performance metric for imbalanced classification problems. The empirical results demonstrate good predictive ability and temporal robustness, achieving an average PR-AUC of 0.82 over the evaluation period. Furthermore, the probabilistic framework is in line with the concept of expected monetary value (EMV) under a binomial state-price interpretation and links the prediction of behavior to the financial decision-making process. The proposed framework provides an interpretable probabilistic methodology for next-transaction prediction under realistic synthetic banking scenarios and establishes a foundation for future validation using real banking transaction data. The results obtained on synthetic transaction data suggest that the inclusion of behavioral consistency in probabilistic models can enhance the stability and practicality of recommending systems for financial institutions.
Copyright (c) 2026 Giovanny Theotista, Ansori Moch. Fandi https://creativecommons.org/licenses/by/4.0
A Proposed Hybrid Deep Learning and Ensemble Learning Model for Predicting Student Dropout in Education
Student dropout remains a major challenge for higher education institutions due to its academic, social, and economic consequences. Early identification of students at risk of dropping out is essential for implementing timely interventions; however, existing prediction approaches often rely on standalone machine learning models that inadequately capture the complex, nonlinear relationships within heterogeneous educational data and exhibit reduced performance under class imbalance. To address these limitations, this study proposes a hybrid prediction framework that integrates deep neural representation learning with a stacked ensemble architecture to improve the accuracy and robustness of student dropout prediction. The proposed framework is evaluated on three benchmark educational datasets. A deep neural network is employed to learn latent feature representations from preprocessed student data, and the resulting embeddings are used to train heterogeneous machine learning classifiers, including XGBoost, Decision Tree, AdaBoost, Extra Trees, and K-Nearest Neighbors. Their probabilistic predictions are subsequently combined through a logistic regression meta-learner to generate the final prediction. To investigate the impact of class imbalance, both class weighting and the Synthetic Minority Over-sampling Technique (SMOTE) are incorporated and systematically evaluated. Model performance is assessed using accuracy, balanced accuracy, precision, recall, F1-score, and ROC-AUC, while permutation feature importance is employed to examine the influence of the original input variables on the overall prediction pipeline, and McNemar's test is used to assess the statistical significance of performance differences. Experimental results demonstrate that the proposed framework consistently outperforms standalone machine learning models, a standalone deep neural network, and conventional stacking approaches across all three datasets. These findings demonstrate that combining deep representation learning with stacked ensemble learning provides a robust and effective framework for early student dropout prediction, supporting data-driven educational interventions and informed decision-making in higher education.
Copyright (c) 2026 Abdulalim M. Ibrahim, Mohamed R. Abonazel, Abdul-Hadi N. Ahmed https://creativecommons.org/licenses/by/4.0
Reinforcement Learning for Dynamic Campaign Budget Optimization
In the age of digital marketing, advertisers must adjust their spending across millions of real-time bids and simultaneously react to highly random users. We present a reinforcement learning approach to maximize campaign budgets with independent decision-making by comparing discrete and continuous control. Using the Mendeley Online Advertisement Click-Through Rate data, we simulate the budget allocation process as a Markov Decision Process where each state encodes context, user, and financial information. Two agents Q-Learning and Deep Deterministic Policy Gradient were implemented and evaluated in identical conditions. We show that DDPG is faster, more stable, better click-through rate, and ROI, compared to Q-Learning. Budget-penalty helps keep budgets in check and save resources without harming user interactions. Our findings indicate that reinforcement learning provides an scalable and interpretable foundation for real-time, adaptive marketing budget optimization.
Copyright (c) 2026 Riad Loukili, Fayc¸al Messaoudi, Manal Loukili https://creativecommons.org/licenses/by/4.0
New Insights into the fourth-order Hankel determinant within a certain class of analytical functions
This paper investigates the subclass $M(\beta, \gamma, \e)$ of analytic functions defined on the open unit disk by employing the principle of subordination as a key analytical tool. Sharp upper bounds are obtained for the coefficients $\left|\y_{\n}\right|$ of functions in this class for $\n=2,3,4,5,6,7$, providing insight into the structure of the coefficients in the subclass. Furthermore, a general expression for the fourth Hankel determinant is derived for functions belonging to $M(\beta, \gamma, \e)$, representing a novel contribution to the field. Several additional results are also established, enriching the theory of analytic functions.
Copyright (c) 2026 Abdullah Alsoboh, Waggas Galib Atshan, Muhammed Salih Muhammed https://creativecommons.org/licenses/by/4.0
Prediction of accident severity for overtaking maneuvers on two-lane roads using Decision Tree, Random Forest, and K-Nearest Neighbors models
Overtaking maneuvers are both dangerous and difficult to perform on two-lane roads; because of the complexity of this maneuver, there is a pressing need to intensify scientific research to find real solutions, therefore, to avert accidents and mitigate the significant mortality and economic expenses involved. This research investigates the use of three machine learning algorithms to predict the severity of accidents depending on the different factors that contribute to accidents, including Random Forest, Decision Tree, and K-Nearest Neighbors. Such variables as road surface, weather, driver gender, type of fuel, etc. are considered to train the prediction models with the accident-related datasets. In order to find out serious predictors of the severity of the accident, the performance of each algorithm is evaluated through a number of measures, such as precision, accuracy, recall, and F1 score. Regarding predictive accuracy, the findings demonstrate that RF is better than the rest of the other models, although there is a minor difference between the results of Decision Tree and KNN. The current research is a credible resource to be utilized by the transportation authorities to mitigate the effects of traffic accidents by showing the effectiveness of the machine learning techniques in predicting the main factors that have the highest impact on the accidents.
Copyright (c) 2026 Sadir Fadhil, Israa Jaddoa https://creativecommons.org/licenses/by/4.0
A New Efficient Biased Estimator in Overdispersed Count Regression for Effectively Handling Multicollinearity in COVID-19 Data from Saudi Arabia
Count data are widely encountered in many scientific fields, particularly in healthcare and epidemiology. One of the most commonly used approaches for analyzing such data is the negative binomial regression model (NBRM), due to its simplicity and effectiveness in modeling event frequencies. Despite its popularity, the presence of severe multicollinearity among explanatory variables can substantially inflate the variance of parameter estimates and reduce the reliability of statistical inference. To address this issue, this study proposes an improved shrinkage estimator for the NBRM, referred to as a novel class of negative binomial Liu-type estimator. The proposed estimator combines the advantages of ridge regression and the Liu estimator, aiming to reduce estimation variance while maintaining stable parameter estimates under conditions of multicollinearity. The proposed estimator is compared with the traditional maximum likelihood estimator, as well as existing ridge and Liu-type estimators, using performance measures such as the mean squared error. Its performance is evaluated through extensive Monte Carlo simulation experiments under different levels of multicollinearity and sample sizes. The simulation results demonstrate that the proposed estimator provides more accurate and stable estimates than the competing methods, particularly in the presence of high multicollinearity. To illustrate the practical applicability of the proposed approach, the method is applied to a real-world healthcare dataset related to COVID-19 cases in the Kingdom of Saudi Arabia. The empirical results confirm the effectiveness of the proposed estimator in improving estimation accuracy and model stability when modeling multivariate healthcare count data. Overall, the proposed estimator offers a useful alternative for modeling multicollinear healthcare count data and enhances the reliability of statistical analysis in applied health research.
Copyright (c) 2026 Eslam H. Hafez, Ali T. Hammad, Ramy Aldallal, Ahmed M. Gemeay https://creativecommons.org/licenses/by/4.0
A Refined Study of T-C-Compactness within T-topological Structures
This research aimed at defining C-compact spaces with three topologies and studying their properties and relationships with other T-topological spaces. The theoretical outcomes are spotlighted and evidence is provided on their generalizability to various well-known theorems related to C-compact spaces. The findings are backed up with illustrative examples.
Copyright (c) 2026 Nabeela Abu-Alkishik, Jafar Ahmed, Diana Mahmoud, Hamza Qoqazeh, Eman AlMuhur https://creativecommons.org/licenses/by/4.0
Mathematical Modeling and Convergence Analysis of Swarm-Based Algorithms in Public Transport Network Optimization
Optimizing public transport networks under dynamic demand and changing operational conditions remains a major challenge due to nonlinear cost structures, fluctuating travel times, and capacity limitations. This study proposes a mathematical optimization framework to represent dynamic public transport networks using multi-criteria objective functions, flow conservation constraints, and time-dependent parameters. Based on this framework, the convergence behavior of Particle Swarm Optimization (PSO) and the Firefly Algorithm (FA) is examined through theoretical analysis under realistic assumptions, including local Lipschitz continuity, bounded feasible regions, and appropriate parameter update schemes. Numerical simulations conducted on various scenarios with demand fluctuations, congestion-sensitive travel times, and random capacity disruptions show that both algorithms converge stably and are able to track changes in optimal solutions. Comparative experiments with other metaheuristic methods further indicate that PSO and FA provide competitive solution quality, robustness, and computational efficiency. Overall, the results demonstrate that a careful alignment between algorithmic mechanisms and network structure enhances convergence reliability and optimization performance, effectively connecting theoretical insights with practical simulation in public transport network optimization.
Copyright (c) 2026 Agustina Pradjaningsih, M. Ziaul Arif, Kiswara Agung Santoso, Ika Hesti Agustin https://creativecommons.org/licenses/by/4.0
On Boundedness, Continuity, and Hyers--Ulam Stability of Interval-Valued Stochastic Processes in a Probabilistic Framework
In this paper, we investigate interval-valued stochastic processes within the framework of pseudo-order relations and analyze their fundamental analytical properties. We give a probabilistically meaningful definition of boundedness for interval-valued processes and use it to establish a single, consolidated local-to-global boundedness theorem, together with a corrected treatment of continuity in probability and of Hyers--Ulam stability for interval-valued stochastic mappings. Every construction that relies on interval subtraction, interval absolute value, or the ordering of negative intervals is given an explicit. The proposed approach provides a consistent structure for handling uncertainty arising from both randomness and interval-valued data, and we illustrate it with a toy financial example in which a return is simultaneously random and only known up to a measurement interval. We show precisely how the developed results relate to, and in the component-wise pseudo-order case reduce to, existing real-valued results, and we discuss open problems for the genuinely set-valued (inclusion-order) setting.
Copyright (c) 2026 Hijaz Ahmad, Waqar Afzal, Mujahid Abbas, Muhammad Tariq, Waleed Mohammed Abdelfattah https://creativecommons.org/licenses/by/4.0
Estimating Value at Risk of the Indonesian Sharia Stock Index using Asymmetric GARCH-MIDAS
This study estimates the market risk of the Indonesian Sharia Stock Index (ISSI) using a volatility framework that combines short-run asymmetric GARCH dynamics and long-run macroeconomic information through the GARCH-MIDAS approach. Daily ISSI closing prices are transformed into logarithmic returns, while monthly inflation and BI Rate are used as low-frequency macroeconomic variables in the MIDAS component. The conditional mean is first represented by an ARIMA process, and the residual variance is then modeled using GARCH(1,1), TGARCH(1,1), GJR-GARCH(1,1), GARCH(1,1)- MIDAS, and GJR-GARCH(1,1)-MIDAS. Value at Risk (VaR) is computed at the 5%, 2.5%, and 1% quantiles, and model adequacy is evaluated with the Kupiec Proportion of Failure (POF) backtesting test. The empirical results show that ISSI returns are stationary around zero, while the closing price series displays a long-run increasing trend with sharp corrections during stress periods. The return series exhibits volatility clustering, ARCH effects, and asymmetric volatility, supporting the use of asymmetric GARCH specifications. The ARIMA(0,0,0) model is selected as the mean model, while GJR-based models provide stronger evidence of leverage effects in the conditional variance. The MIDAS results indicate that inflation and BI Rate are not statistically significant determinants of the long-run volatility component at the 5% level, suggesting that short-run market shocks dominate the volatility process during the sample period. The Kupiec POF results indicate that GJR-GARCH-MIDAS models with inflation and BI Rate are accepted at the 2.5% and 1% VaR quantiles, making them the most consistent specifications for downside risk estimation of ISSI. The findings confirm that incorporating asymmetric volatility is important for improving VaR estimation in the Indonesian Islamic equity market.
Copyright (c) 2026 Darmanto Darmanto, Abril Hisyam Efendi https://creativecommons.org/licenses/by/4.0
Plithogenic Vertex Graph and Neutrosophic Vertex HyperGraph
A Plithogenic Set extends the classical fuzzy, intuitionistic, and neutrosophic paradigms by assigning attribute-based membership and contradiction values to elements, and the same ideas naturally extend to graph-based structures such as the Plithogenic Graph. Neutrosophic sets, in turn, represent elements with independent degrees of truth, indeterminacy, and falsity on the unit interval, thus handling incomplete, inconsistent, and ambiguous information effectively. This paper investigates the Plithogenic Graph, the Plithogenic Vertex Graph, and the Plithogenic Edge Graph. In addition, it examines the Intuitionistic Fuzzy Vertex Graph and Edge Graph, the Neutrosophic Vertex HyperGraph and Neutrosophic Edge HyperGraph, and the Neutrosophic Vertex SuperHyperGraph and Neutrosophic Edge SuperHyperGraph.
Copyright (c) 2026 Takaaki Fujita, Raed Hatamleh, Ajoy Kanti Das https://creativecommons.org/licenses/by/4.0
Modeling the Gender Gap in Children’s Educational Expenditure in Egypt: An Econometric Hurdle Model Approach
The study used nationally representative survey data from the 2019–2020 Household Income, Expenditure, and Consumption Survey (HIECS), conducted biennially in Egypt by the Central Agency for Public Mobilization and Statistics (CAPMAS). The sample consisted of 14,892 children from 6,721 Egyptian households. Descriptive statistics were used to summarize the demographic, social, and economic characteristics associated with household educational expenditure. A hurdle model was employed to analyze gender differences in educational spending, distinguishing between two stages: (1) the likelihood of incurring any educational expenditure, and (2) the level of educational expenditure on children among households with positive spending. In the first stage, female children aged 4–8 and 14–18 years were less likely to receive educational expenditure, whereas those aged 9–13 years were more likely to receive such expenditure. In the second stage, among households with positive spending, females received lower educational expenditure during early and middle childhood but higher expenditure during adolescence.
Copyright (c) 2026 Shaimaa Kh. Abdel-Maged, Mohamed N. Abdel Fattah, Suzan Abdel-Rahman https://creativecommons.org/licenses/by/4.0
Missing Values Imputation for Spatio-Temporal Climate Variable using Graph Convolutional Networks (GCN)
Climatic time series analysis often suffers from an issue of missing values. Which directly affects the accuracy of analysis and modeling especially with multi-site data which include spatial and temporal dependencies. Although traditional imputation methods do exist, most of them depend mainly on temporal information which limits its efficiency especially with consecutive missing values or when the temporal data are weak. This limitation becomes more critical under the Block Missing Pattern, where consecutive missing values reduce the availability of temporal information and increase the difficulty of the imputation process. This study applies a spatially structured Graph Convolutional Network (GCN) model for missing-value imputation in multivariate climatic time series collected from multiple meteorological stations. covering the period from 1994 to 2024 with a total of 11,329 daily observations. To reduce seasonal heterogeneity, the data were first stratified into hot and cold seasons. Each seasonal dataset was then divided into training (80\%), validation (10\%), and testing (10\%) subsets for model development and performance evaluation. Overall, this study confirms that the use of models that depend on spatial structures is an effective method for handling missing value in time series, and paves the way for developing more advanced models that combines between the spatial and temporal dimensions. The model’s performance has been evaluated under the Block Missing Pattern in both of the hot and cold seasons using RMSE, MAE and MAPE metrics, as well as comparing the performance with K-Nearest Neighbors (KNN), Linear Interpolation (LI), and GRU-D. The results show that the Graph Convolutional Network provides a more efficient representation of the climatic data, especially in cases with weak or unavailable temporal data, where a higher accuracy can be achieved by using spatial relationships between stations.{The proposed model achieved the lowest error values across most climatic variables in both seasons} The results also indicate that the nature of the data plays an essential role in the performance, where the decrease of variability and the stability of values in the hot season contributed to the improvement of the missing data imputation.
Copyright (c) 2026 Baraa Wisam Abdulghani, Osamah Basheer Shukur Shukur https://creativecommons.org/licenses/by/4.0
Efficient Cloud Task Scheduling Framework Based on M/M/N Queueing Optimization
Task scheduling in the cloud data center is an optimization problem that aims to improve the resource utilization and hence reduces the response time. It faces challenges in assigning tasks to a suitable virtual machine such as throughput, waiting time and degree of imbalance. This paper presents a multi-objective algorithm for task scheduling problem based a novel M/M/N optimization model. The goal is to identify the optimal task scheduling strategy that 1) decreases response time by minimizing the volume of pending requests; 2) mitigates performance degradation by maintaining the service level agreement (SLA) at the desired standard; and 3) enhances load balancing by ensuring servers operate at optimal utilization levels. The results show that the proposed algorithm has better results in response time, throughput and load imbalance when compared to other existing algorithms.
Copyright (c) 2026 Zaid Sabeeh, E. I. Elsedimy, Ahmad Hailan, Nabaa Abd https://creativecommons.org/licenses/by/4.0
ECG Image Classification Using Hybrid Machine Learning and Deep Learning Techniques
Electrocardiogram (ECG) interpretation is one of the most important diagnostic procedures in cardiovascular medicine. It is routinely used to detect myocardial infarction, rhythm disorders, conduction abnormalities, ischemic changes, and previous cardiac injury. However, accurate interpretation of ECG reports still depends greatly on physician experience, especially when ECG records are stored as scanned documents, printed sheets, or image captures rather than raw digital waveform signals. This creates a practical need for automated image-based ECG classification systems that can support physicians in diagnosis, screening, triage, and telemedicine applications. This paper presents a hybrid framework for four-class ECG image classification using enhanced grayscale preprocessing, advanced handcrafted feature extraction, hyperparameter-optimized machine learning, and deep learning comparison. The dataset contains four diagnostic categories: myocardial infarction (MI), abnormal heartbeat (AHB), history of myocardial infarction (HMI), and normal ECG. The proposed system automatically detects the ECG chart region, crops the useful waveform area, removes background artifacts, resizes all images to a fixed resolution, balances the training data through augmentation, extracts discriminative image features, and evaluates multiple classifiers. Experimental results show that the Support Vector Machine (SVM) model achieved the highest classification accuracy of 96.76%, followed closely by the Convolutional Neural Network (CNN) model with 96.40%. These results demonstrate that optimized classical machine learning can remain highly competitive with deep learning when supported by strong preprocessing and informative feature engineering. The proposed framework can serve as a reliable foundation for intelligent ECG screening systems and computer-aided cardiac diagnosis.
Copyright (c) 2026 Osama H. M. Assass, BenBella S. Tawfik, Mohamed M. El-Gazzar, Amal M. M. Elnawsany https://creativecommons.org/licenses/by/4.0
Monitoring Changes in the Blocks of One of a Bridges in Nasiriyah Using Specific Quality Control Charts
Monitoring the quality of concrete blocks is a fundamental aspect of construction projects to ensure the safety and stability of structures. This research aims to apply a combination of traditional statistical control charts, along with a regression control chart, to monitor the quality of concrete block and diagnose the behavior of the production process, without seeking to compare the superiority of these charts. The research relied on real data consisting of (118) observations of concrete blocks tests for a bridge in Nasiriyah, obtained from the consulting office at Thi Qar University. The arithmetic mean, standard deviation, range, and moving average charts were applied, in addition to a regression chart for control purposes, by using the traditional control limits established in Shewhart charts, the results showed that the traditional control charts respond to different patterns of change in quality characteristics. The mean and moving average charts revealed changes in average compressive strength, while the standard deviation and range charts confirmed the stability of dispersion within the production process. The results of the regression control chart also showed the stability of the linear relationship between the weight of the concrete blocks and the explanatory variables related to the production and inspection process, , without the appearance of signals outside the limits of control. The research results indicate that the use of an integrated set of statistical control charts provides a more comprehensive monitoring and diagnostic framework for monitoring the quality of concrete blocks, highlighting the complementary role that the control regression chart plays alongside.
Copyright (c) 2026 Asmaa Ghalib Jaber, Fahad Husseian Enad, Loaiy F. Naji https://creativecommons.org/licenses/by/4.0
An IoT-Enabled Smart System for Real-Time Environmental Monitoring and Automated Threshold-Based Irrigation for Water Stress Testing in Precision Agriculture
Internet of Things is a major technology for improving agricultural practices and devel-oping intelligent research environments dedicated to biologists. Real-time monitoring of environmental parameters promotes efficient crop management and data-driven deci-sion-making. Nonetheless, the majority of existing IoT systems are limited to data visu-alization or alert generation, they rarely exploit the full potential of measurements for automation. In this context, this study proposes an intelligent system enabling not only real-time monitoring of environmental parameters but also automated irrigation man-agement through water stress scenarios defined by biologists in a controlled environment. Our system integrates an IoT measurement unit and a microservices-based web applica-tion for advanced management and analysis. The evaluation results after the deployment of a single unit in an agricultural field indicate that our IoT system provides measure-ments with effective stability, reflected by a low variance of 0.0721 for soil moisture, 0.0552 for soil temperature, 0.0081 for air temperature and 0.0023 for air humidity, and executes 50000 water stress scenarios in 872 ms without any error. These results highlight the system’s capability for data-driven automation, decision support and environmental sustainability in precision agriculture.
Copyright (c) 2026 Mohamed Walid HAJOUB, Hicham Touil, Mohammed Achkari Begdouri https://creativecommons.org/licenses/by/4.0
Characterizations of Truncated Univariate Continuous Distributions Introduced by Lorenzo Zaninetti, I-XV
This paper deals with various characterizations of 15 truncated univariate continuous distributions proposed by Lorenzo Zaninetti from 2019-2025, all published in the "International Journal of Astronomy and Astrophysics". Zaninetti masterfully and with a lot of super complicated derivations presents the cdfs and pdfs for these distributions. The present work may be considered as a supplement to Zaninetti's excellent works. These characterizations are based on: $\left( i\right) $ a simple relationship between two truncated moments and $\left( ii\right) $ the reverse hazard function and. It should be mentioned that for the characterization $\left( i\right) $ the cumulative distribution function need not to have a closed form and depends on the solution of a first order differential equation, which provides a bridge between probability and differential equation.
Copyright (c) 2026 G. G. Hamedani, Amin Roshani https://creativecommons.org/licenses/by/4.0
One-inflated zero-truncated Binomial distribution: Distributional Properties, Estimation and Applications
The analysis of zero-truncated count data plays a vital role in various fields, such as public health, ecology, manufacturing and social sciences.This work introduces a one-inflated extension of the zero-truncated binomial distribution, framed as a two component model. Some of the key statistical properties of the distribution, including moments, generating functions, skewness, kurtosis and index of dispersion are derived. Also characterization of the proposed distribution based on conditional distribution is presented. Different estimation strategies are discussed and a simulation study is conducted to assess the performance of the maximum likelihood estimators. Finally to test the compatibility of our model we test two real-life datasets.
Copyright (c) 2026 Kalpasree Sharma, Partha Jyoti Hazarika, Morad Alizadeh https://creativecommons.org/licenses/by/4.0
A Data-Driven Decision Support Framework for Business Intelligence Systems Leveraging BERT-Enhanced LLMs
Many techniques were introduced to intimate the gap existing between raw organizational data of business intelligence systems and practical decision support. One of these techniques is to integrate the large language models (LLMs) into enterprise business intelligence (BI) architectures. The available BI frameworks suffer from bi-direction drawbacks. These drawbacks represented by treat natural language query interfaces as peripheral additions or by failing in providing empirically confirmed and decision-quality outcomes. This will put practicians without any principled basis for architectural investment. This paper addresses both limitations. We present a three-layer BI-Enabled Decision Quality (BIDQ) framework that formalizes the interdependencies among data readiness, analytical capability, and decision integration, and we introduce a BERT-based query understanding module trained on 180,000 enterprise query--response pairs drawn from five production systems (SAP ERP, Salesforce CRM, Oracle SCM, customer service ticketing, and financial reporting). The BERT model achieves 86.5% test accuracy, an F1-score of 86%, and a mean inference latency of 45 ms a 5.5-percentage-point accuracy improvement over the best LSTM baseline (p < 0.001, Cohen's d = 0.42). Training converged in 72 GPU-hours on eight NVIDIA A100 GPUs using AdamW optimization with linear warmup and decay. To ground the framework in measurable outcomes, we conducted a twelve-month field study across five industry sectors (retail, banking, healthcare, manufacturing, and logistics), finding an average 26-percentage-point improvement in decision quality and an 89% reduction in decision cycle time following BI deployment. Financial modelling indicates a typical break-even point at approximately month 12 of deployment. These results determine the quantitative criterions that can guide the organizations in formalization and structuring the investments and measuring of LLM-augmented BI.
Copyright (c) 2026 Alaa Abid Muslam Abid Ali https://creativecommons.org/licenses/by/4.0
A Highly Accurate Upper Bound for the Cumulative Standard Normal Distribution Function and Related Functions
Although there is extensive literature concerning upper bounds for the cumulative standard normal distribution function Φ(x), many existing bounds are not uniformly accurate over the entire domain x ≥ 0. This paper proposes a new explicit upper bound for Φ(x) that is uniformly accurate over x ≥ 0 and improves the classical Polya upper bound and several related bounds available in the literature. The proposed bound is constructed by modifying the classical Polya structure through the introduction of a polynomial correction term inside the exponential function, leading to a flexible parametric form. The coefficients of this correction polynomial are determined using a constrained minimax-type optimization framework combined with local accuracy conditions obtained through Taylor-series matching near the origin, ensuring both global tightness and local stability. It is shown that the maximum absolute error between the proposed bound and the exact function Φ(x) does not exceed 5.785 × 10^(−5) for all x ≥ 0. Numerical comparisons and graphical illustrations demonstrate that the proposed bound is consistently tighter than several well-known alternative bounds, particularly in the small and moderate range of x. The structure of the bound also preserves analytical tractability, making it suitable for theoretical analysis and computational applications. Finally, due to the functional relationships between Φ(x), the Q-function and the error function, the proposed construction can be directly extended to derive corresponding bounds for these related special functions.
Copyright (c) 2026 Omar M. Eidous https://creativecommons.org/licenses/by/4.0
A Statistical Evaluation Framework with Application to YOLO-Based Models in Cross-Language License Plate Recognition: Arabic and Latin Alphabets
This study presents a statistical evaluation framework with application to YOLO-based models in cross-language License Plate (LP) recognition. Rather than relying solely on conventional accuracy metrics, the proposed framework integrates statistical performance analysis with deep learning techniques to provide a comprehensive and objective comparison of object detection models. The framework employs multiple statistical evaluation measures, including precision, recall, mean Average Precision (mAP), confusion matrices, and confidence-based performance curves, to quantify detection accuracy, classification reliability, and model robustness. Experiments were conducted using an Iraqi license plate dataset comprising 1,834 annotated images containing Arabic and Latin license plate characters. Two state-of-the-art object detection models, YOLOv5 and YOLOv8, were trained and evaluated under identical experimental settings. The results demonstrate that YOLOv8 consistently outperformed YOLOv5 across all statistical evaluation measures, achieving higher detection accuracy and improved classification performance. The proposed statistical evaluation framework provides a robust and objective approach for assessing and comparing deep learning models. By integrating statistical evaluation principles with advanced object detection techniques, this framework offers an effective methodology for model evaluation and selection in cross-language license plate recognition and can be extended to various computer vision applications.
Copyright (c) 2026 Israa Lewaaelhamd, Ahmed Elaraby https://creativecommons.org/licenses/by/4.0
Globally convergent conjugate gradient algorithms for large-scale unconstrained optimization
Optimization techniques are extensively employed to obtain numerical solutions to optimal control problems that appear in scientific and engineering computations, particularly in the context of large-scale problems. In this paper, drawing on some modern and computationally efficient approaches, we introduce two modified conjugate gradient methods (referred to as the IHS and IPRP methods) for unconstrained optimization. Under the strong Wolfe line search (SWLS), the proposed methods are shown to generate sufficient descent at every iteration. Furthermore, we establish that these methods are globally convergent for arbitrary objective functions, provided that the line search satisfies the strong Wolfe conditions. Numerical experiments, interpreted using the Dolan and Mor\'{e} performance profiles, confirm the efficiency of the IHS and IPRP methods in comparison with several existing algorithms.
Copyright (c) 2026 Mehamdia Abd Elhamid, Ali Joma Alissa, Basim A. Hassan, Bouaziz Tayeb, Ismat Suleiman Salem Abu Sahyoun https://creativecommons.org/licenses/by/4.0
Power Flow Analysis in Unbalanced Distribution Networks with Single-Phase and Two-Phase Laterals Using a Fixed-Point Formulation Implemented in MATLAB
This paper proposes a fixed-point methodology for power flow analysis in unbalanced three-phase distribution networks featuring mixed single-phase, two-phase, and three-phase laterals—a common configuration in rural and low- voltage distribution systems. The method addresses the convergence challenges posed by low X/R ratios, radial topologies, and inherent phase unbalance that hinder classical power flow solvers. The formulation employs a per-unit impedance model, constructs a detailed three-phase nodal admittance matrix, and incorporates explicit mathematical models for wye and delta load connections. A fixed-point iterative scheme updates nodal voltages using a partitioned network representation. The algorithm was implemented in MATLAB® and validated against the industry-standard software DIgSILENT PowerFactory® using three test systems of 4, 8, 25 and 124 nodes. Results demonstrate exceptional accuracy, with voltage magnitude deviations below 2 × 10−3 p.u., phase angle differences under 0.05◦, and relative power balance errors consistently below 0.05%. The method exhibits robust and rapid convergence, reaching a tolerance of ε = 1 × 10−10 in only 4 iterations across all test cases, with near-linear computational scalability. This was observed with standard load systems, but to test the methodology’s performance with heavy load systems, the active power consumed by the loads was increased tenfold. We observed that execution times and the number of iterations increased, and also that, as the system load increased, the method became less contractive. This combination of high numerical precision, deterministic convergence, and low computational cost establishes the proposed fixed-point method as an effective and reliable tool for the analysis and planning of modern, unbalanced distribution networks.
Copyright (c) 2026 Cristian David Cuervo-Gonzalez, John Fredy Pinilla-Morales, Oscar Danilo Montoya Giraldo https://creativecommons.org/licenses/by/4.0
A Semi-Analytical Approach to Solving the Ornstein--Uhlenbeck Equation via Reproducing Kernel Hilbert Spaces (RKHS)
This work proposes a semi-analytical method to approximate the solution of the Ornstein--Uhlenbeck (OU) stochastic differential equation by means of Reproducing Kernel Hilbert Spaces (RKHS). The OU process, widely used as a Gaussian mean-reverting model in physics and finance, is decomposed into a deterministic mean-reversion component and a purely stochastic component represented as an It\^o integral. The deterministic part is reconstructed via regularized kernel regression, whereas the stochastic term is modeled through an autoregressive representation in an RKHS induced by a positive definite kernel. By combining the representer theorem with a kernel-based autoregressive scheme, we obtain a finite-dimensional linear system for the kernel coefficients and a fixed-point algorithm for solving the preimage problem. The proposed framework yields a fully kernel-based approximation of the OU dynamics. The methodology is validated on synthetic Ornstein--Uhlenbeck trajectories and, subsequently, on real time series data, illustrating that the RKHS-based approach accurately captures the mean-reversion behavior and stochastic variability of the process while preserving numerical stability and interpretability.
Copyright (c) 2026 Erisbey Marin, Sebastian Arbelaez , Andres Felipe Valencia https://creativecommons.org/licenses/by/4.0
The Dual Simplex Method for Solving Bounded-Variable Linear Programs
In this paper, we {propose a novel and robust} dual simplex algorithm for solving bounded-variable linear programming based on a structured dual support framework and an enhanced long-step rule. The method enables stable and efficient updates of both the support and the associated co-solution. Its effectiveness is supported by a rigorous theoretical analysis: we prove a sign-preservation property of the support co-solutions and derive an explicit expression for the variation of the objective function, ensuring controlled and monotonic improvement and yielding strong convergence guarantees. Extensive experiments on NETLIB problems benchmarks demonstrate that the proposed approach is competitive with the dual simplex implementation of GLPK, confirming its efficiency and reliability.
Copyright (c) 2026 Khalil DJELOUD https://creativecommons.org/licenses/by/4.0
Distributed Lag Fixed Effects and Non-Additive Response Surface Inference for Intergovernmental Fiscal Transfers: Evidence on Food Security
This study examines how intergovernmental fiscal transfers relate to food security when effects may be delayed and instruments may operate non-additively. Using panel data on 435 Indonesian districts/cities in 2018–2023 (N = 2,610), we estimate fixed-effects distributed-lag models for DAK-Irrigation, DAK-Agriculture, and the Village Fund, then fit post-2020 interaction models. To keep interactions interpretable, we evaluate marginal effects over observed transfer combinations and complement the baseline with checks for common shocks, extreme values, and placebo threats. Three findings emerge. First, irrigation associations are timing-sensitive: near-term coefficients are negative, whereas the two-year lag is more favorable. Second, Village Fund associations are most consistent with a one-cycle (t-1) pattern, although they remain sensitive to aggregate time variation. Third, agriculture terms are weak. The post-2020 response surface suggests flattening at high joint transfer intensity, but implied FSI shifts are small, marginal effects remain indistinguishable from zero, and placebo diagnostics do not support a causal interaction block. Because 2020–2023 coincides with the COVID-19 crisis and recovery, any non-additivity may reflect crisis-era coordination stress rather than a normal-state relationship. Overall, the paper offers a framework for evaluating lagged fiscal programs in short panels and interprets estimates as within-district associations rather than causal effects.
Copyright (c) 2026 Yeni Nuraeni, Muhammad Fazri, Andi Risdawati Alwi Paluseri, Alkadri, Syarah Siti Supriyanti, Cita Pertiwi, Febrina Elia Nababan, Yakobus Supriyanto, Adelia Oktarina, Emi Syarif https://creativecommons.org/licenses/by/4.0
Artificial intelligence adoption in tourism: A systematic literature review of customer experience
This study systematically reviews the current state of artificial intelligence (AI) applications in the tourism sector and their role in enhancing customer experience. It examines how AI is used to optimize operations and personalize services and explores factors that drive or hinder AI adoption. Following PRISMA guidelines, the review covers 57 articles published from 2000 to 2024, sourced from Web of Science and Scopus, based on defined inclusion criteria. The review also includes a bibliometric analysis of publication patterns and keyword co-occurrence using VOSviewer to identify prominent research themes and gaps. The analysis reveals eight thematic clusters: AI applications (e.g., demand forecasting, dynamic pricing, service automation, personalization) and associated challenges (technical, financial, socio-ethical). While AI offers significant opportunities to enhance tourism operations and customer experience, issues of data privacy, ethics, and workforce displacement remain prominent. These findings are synthesized into an integrative conceptual framework that links technological, ethical, and contextual factors influencing AI adoption. The study provides practical insights for tourism managers, highlighting the need to balance technological innovation with ethical considerations and to adapt AI strategies to diverse cultural contexts.
Copyright (c) 2026 Statistics, Optimization & Information Computing https://creativecommons.org/licenses/by/4.0
Machine Learning in Education: An Overview of Applications and Datasets
Artificial intelligence (AI) is a field of research that has attracted a lot of attention in the past few years due to its rapid development and global reach. From integrating AI into everyday life, technology, and its use has drastically changed along with the understanding of the world surrounding us. Its application ranges from automating mundane tasks to making crucial AI-driven decisions in medicine, entertainment, finance, and even retail. One of the most positive and profound areas of development in AI is in education because if developed further, it can change the ways education is rendered, enhance the quality of education, and most importantly, tailor the learning process according to the needs of students all over the world. AI is not a fad when it comes to educational learning but a very essential shift regarding how information is taught and learned. The goal of this paper is to analyze several applications of Machine Learning (ML), a subfield of artificial intelligence, in the context of education. In particular, we want to focus on the variety of ML by defining supervised learning, unsupervised learning, reinforcement learning, and also deep learning, and their importance to the improved practice of education. These techniques will be further explored in terms of real-life application in teaching and learning processes through illustrative case studies. These case studies will illustrate how ML models aid in customizing learning, increasing student participation, and enhancing collaboration.
Modified cross-validation procedure in selection shrinkage parameter of Poisson ridge regression model
Poisson regression is a fundamental framework for modeling count data; however, the maximum likelihood estimator becomes unreliable under multicollinearity, yielding unstable estimates with large variance and weak inferential performance. Ridge regression offers a remedy by shrinking regression coefficients, but its effectiveness depends crucially on selecting an appropriate shrinkage parameter. This paper proposes a modified cross‑validation (MCV) procedure for Poisson ridge regression that explicitly targets this instability. The method repeatedly reassigns observations to folds, re‑computes the optimal shrinkage parameter over many replications, and then uses an appropriately chosen quantile of the resulting distribution of optimal values as the final tuning parameter; the quantile is itself selected via cross‑validated prediction error. Extensive Monte Carlo simulations, conducted over varying sample sizes, numbers of predictors, intercept values, and correlation levels, show that the MCV‑based Poisson ridge estimator consistently achieves lower mean squared error than maximum likelihood, standard cross‑validation, and generalized cross‑validation, with particularly notable gains under strong multicollinearity. A real data analysis further demonstrates that the proposed procedure improves prediction performance in practical count data applications.
Resilience and Divergence: A Comparative Analysis of Life Insurance Markets in Germany and Japan during the COVID-19 Pandemic and the Interest Rate Trend Reversal (2020–2022)
Background: Germany and Japan have "mirror" life insurance ecosystems with very old populations and long periods of low interest rates. From 2020 to 2022, these markets went through a "double-cycle" crisis: the biological and operational shocks of the COVID-19 pandemic, followed by a major shift in the macroeconomic trend. Objective: This study examines the differing resilience strategies of two mature markets, highlighting the impact of distinct regulatory frameworks, Germany’s Solvency II and Japan’s Standard Reserve system, on their adaptation to global instability. Methods: This research employs primary regulatory data from the Federal Financial Supervisory Authority (BaFin) and the Life Insurance Association of Japan (LIAJ) to conduct a comparative mixed-methods analysis. The analysis integrates a Pearson correlation matrix with a quasi-experimental Interrupted Time Series (ITS) design and counterfactual modelling to ascertain the causal impact of the 2022 macroeconomic shock. Main Points: The analysis identifies two path-dependent survival models: Japan (Commercial Agility): The market bounced back thanks to sales, as shown by the strong positive correlation between Gross Written Premiums (GWP) and new policies (r = 0.749). ITS modeling showed that the slope would speed up significantly after 2022, by 1.60 trillion per quarter. This was due to a shift in strategy to "Third Sector" (medical/cancer) products and a 31.9% allocation to foreign securities. Germany (Regulatory Fortification): Resilience was based on capital adequacy instead of volume. There was almost a perfect correlation (r = 0.944) between GWP and Solvency Capital Requirement (SCR) coverage, which rose to 526.0% in 2022. At the same time as this solvency "windfall," there was an "Interest Rate Paradox." Rising rates made regulatory ratios stronger, but they also destroyed the value of fixed-income portfolios, turning €51.6 billion in hidden reserves into €19.4 billion in net hidden liabilities.
Model Selection Criteria CV, UBR, and GCV for a Mixed Truncated Spline–Gaussian Kernel Estimator in Health Modeling
Nonparametric regression modeling has commonly applied a single estimator to all predictor variables. Although this approach is straightforward, it can be overly restrictive because predictors often exhibit heterogeneous relationships with the response variable, including linear trends, smooth nonlinear patterns, abrupt changes, or localized variations. Using a uniform estimator may therefore limit model flexibility and reduce predictive accuracy. To overcome this limitation, this study investigates a Mixed Truncated Spline–Gaussian Kernel Estimator, which allows each predictor to be modeled using the estimation technique most appropriate to its underlying data structure. The main objective of this research is to compare the performance of three smoothing parameter selection criteria, namely Cross-Validation (CV), Unbiased Risk (UBR), and Generalized Cross-Validation (GCV). These criteria are employed to determine optimal smoothing parameters, including the number and locations of knots in the truncated spline component and the bandwidth in the Gaussian kernel component. The empirical analysis is conducted using health-related data on heart disease risk factors, a domain characterized by complex and potentially nonlinear relationships. The results indicate that models incorporating three knot points consistently outperform alternative specifications. This superior performance is reflected in lower values of Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE), as well as higher coefficients of determination (R²). Among the selection criteria examined, GCV yields the most accurate and stable model, outperforming both CV and UBR. From a methodological perspective, this study contributes to nonparametric regression by providing a systematic evaluation of smoothing parameter selection within a mixed estimator framework. From an applied standpoint, the proposed approach enhances the modeling of heart disease risk factors by offering greater flexibility and precision. Furthermore, the findings support Sustainable Development Goal (SDG) 3: Good Health and Well-Being by promoting robust, data-driven methods for evidence-based health policy formulation.
Exploring sigma coloring within lexicographic products of innovative graph families
Sigma coloring is a vertex coloring strategy that distinguishes adjacent vertices by the sum of colors in their open neighborhood. The minimum number of colors needed in a sigma coloring of a graph $G$ is called its sigma chromatic number $\sigma\left(G\right)$. The bounds of $\sigma\left(G\right)$ are sharp. For every graph $G$, $\sigma\left(G\right)\leq\chi\left(G\right)$. In this article, a clear investigation is made under sigma coloring on the lexicographic product of a path graph with various graph families including path, cycle, star, double star, and comb graphs. Additionally, the sigma chromatic number for each case is found and verified with chromatic number for bounds.
A Pseudo-Gaussian Test for Comparing Periodic Coefficient Regression Models with Consecutive Periods
This paper addresses the problem of selecting appropriate periods in periodic coefficient regression models where different regressors may exhibit distinct periodic structures. While existing approaches assume a common period across all variables, real-world applications often involve multiple periodicities. We propose a pseudo-Gaussian test for comparing a periodic regression model with variable-specific periods S_j against a model with periods S_j+1, providing a formal framework for local refinement of period specifications. The test is developed within a small T, large n asymptotic framework using uniform local asymptotic normality (ULAN), and we derive the least squares estimator for model parameters under the null hypothesis. Extensive simulation studies demonstrate the test's validity and power across symmetric and asymmetric error distributions, as well as its superiority compared to the likelihood ration test. Comparisons with AIC and BIC reveal competitive performance in period selection. An application to real meteorological data illustrates how the proposed test can be used sequentially to identify optimal periods, with the results corroborated by RMSE-based model selection. The method offers a flexible and robust tool for model diagnostics in settings with complex periodic structures.
SUR-SAR Modelling of the Human Development Index (HDI) of Regencies and Municipalities in West Java Province, Indonesia
This study examines regional disparities in the Human Development Index (HDI) across 27 regencies and municipalities in West Java Province from 2022 to 2024 using a Spatial Seemingly Unrelated Regression–Spatial Autoregressive (SUR–SAR) model. HDI in West Java exhibits both spatial clustering and temporal persistence, indicating that development outcomes are shaped by interregional interactions and structural continuity across years. To capture these dependencies, the analysis incorporates eight socio-economic variables such as poverty, availability of health facilities, population density, inequality, unemployment, consumption level, GRDP per capita, and regional minimum wage into a multi-equation spatial system estimated using Maximum Likelihood. Four model classes are evaluated sequentially that is OLS, SUR, SAR, and SUR–SAR. The results demonstrate that the SUR–SAR specification provides the best overall fit and effectively accounts for both spatial dependence and inter-year error correlations. Population density consistently emerges as the strongest positive and highly significant determinant of HDI, while poverty shows a stable negative effect across multiple models. Minimum wage becomes significant only within the SUR–SAR framework, indicating that the influence of labour income on human development is more apparent once spatial and temporal linkages are jointly modelled. The spatial lag parameter is positive and significant across all equations, confirming the presence of HDI spillover effects among neighboring regions.
SEC-ToLeD: A Super-Efficient and Compact CNN for Tomato Leaves Diseases Detection for Resource-Constrained Devices
Tomato leaf diseases significantly affect agricultural productivity and require accurate yet lightweight detection models suitable for real-world deployment. In this study, a super-efficient and compact convolutional neural network, named SEC-ToLeD, is proposed for tomato leaf disease classification using the PlantVillage dataset. The model integrates depthwise separable convolutions with squeeze-and-excitation blocks to enhance feature representation while maintaining low computational complexity. SEC-ToLeD contains only 219,651 trainable parameters with a compact size of 0.84 MB. Experimental results demonstrate 99.75% testing accuracy, 99% precision, recall, and F1-score, with an average inference time of 1.105 ms per image. Compared to existing lightweight models, SEC-ToLeD achieves superior performance while being significantly smaller in size, making it highly suitable for real-time deployment on resource-constrained and edge devices.
Beyond Engagement: Classifying News Impact on X Using Impressions Data and Hybrid Feature Architecture
Predicting news reach on X (formerly Twitter) is a critical challenge for digital journalism. However, existing literature often relies on biased ``proxy metrics'' (likes, retweets) that miss the ``silent majority'' of passive viewers. This study presents an empirically validated multimodal framework for objective news impact classification using Impressions (View Count) as ground truth. We construct a time-stratified dataset from @detikcom on X (2022--2025), labeled into ”Regular,'' “Hot,'' and ”Viral'' classes via a data-driven log-base-10 thresholding strategy. A six-scenario ablation study evaluates our proposed Adaptive Gated Cross-Modal Fusion architecture against static baselines, contextual extractors, and standard MLP fusion variants. This architecture learns to dynamically balance textual and engagement signals via a trainable gating mechanism. The proposed model (S6) achieves a Macro F1-Score of 0.7383 (peak Viral-class F1 of 0.6277), representing an 85.8% improvement over the unimodal text-only baseline. A dual-track IndoBERT vs. mBERT comparison confirms the necessity of monolingual pre-training. Furthermore, forensic error analysis demonstrates how “semantic awareness'' resolves Cold Start underestimation and False Viral overestimation that consistently cause tree-based classifiers to fail.
Integrating Statistical Clustering Methods and Machine Learning to Uncover Latent Population Profiles in Childhood Disability
Childhood disability in low-resource settings is shaped by intersecting socioeconomic and geographic disadvantages, yet traditional regression approaches cannot capture how these factors combine to form distinct population subgroups. Using nationally representative MICS6 data from the Central African Republic, this study integrates two complementary unsupervised learning methods, Latent Class Analysis (LCA) and Partitioning Around Medoids (PAM) with Gower distance, to identify latent vulnerability profiles among 6,167 children aged 5--17 years. All analyses were conducted within a survey-weighted framework to account for the complex sampling design. Model selection using BIC/AIC (LCA) and weighted silhouette and Calinski--Harabasz indices (PAM) consistently supported a six-cluster structure. These clusters reflected meaningful combinations of household wealth, maternal education, residential setting, and region, with disability prevalence ranging from 28--43\% across clusters. Although individual-level agreement between LCA and PAM assignments was minimal, both methods revealed convergent population-level patterns, including one small, highly vulnerable subgroup with markedly elevated disability burden. The findings demonstrate the methodological value of triangulating model-based and distance-based clustering to uncover hidden structures in complex survey data and provide new insights into the multidimensional nature of childhood disability in fragile settings.
Predicting Lung Cancer Using Clinical Data and Machine Learning
Early and accurate lung cancer detection remains a critical challenge in medical diagnostics. This study investigates the application of multiple machine learning classifiers combined with data resampling and hyperparameter optimization techniques for improved predictive performance on lung cancer datasets. Synthetic Minority Oversampling Technique (SMOTE) was employed to resolve severe class imbalance, while Jellyfish Search Optimizer (JSO), a metaheuristic algorithm, was utilized to optimize hyperparameters, particularly of the Random Forest classifier. Experimental results indicate that integrating SMOTE and JSO enhances model generalizability and predictive accuracy, with the Random Forest classifier demonstrating the highest improvement accuracy increasing from 91.94% to 95.16% and F1 score from 95% to 97%. Other classifiers such as AdaBoost and Decision Trees experienced minimal performance changes, signifying that ensemble methods combined with optimization are promising for lung cancer prediction tasks. Multiple metrics including Accuracy, F1 score, ROC_AUC, and PR_AUC were used to provide comprehensive evaluation, underscoring the robustness of the proposed methodology. The findings suggest potential applications in clinical decision support systems.
Towards Transparent AI for Lung Cancer Diagnosis: A Dual-Pipeline Explainable Framework Using Clinical and CT Imaging Data
In recent years, artificial intelligence (AI) has shown promising performance in medical diagnosis; however, its clinical adoption remains limited due to a lack of interpretability. In this study, we propose a dual-pipeline explainable framework for lung cancer diagnosis using two independent data modalities: structured clinical data and CT imaging data. For the clinical data, several machine learning models were compared, such as LightGBM, CatBoost, XGBoost, Random Forest, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes. For the imaging data, deep learning models such as VGG16, ResNet50, InceptionV3, MobileNetV2, DenseNet121, and Xception were compared using the IQ-OTH/NCCD dataset. To ensure the reliability of the validation results, a strict patient-level split was used to avoid data leakage. The experimental results showed that LightGBM obtained the best results on the clinical data, achieving an accuracy of 98.39% and an ROC-AUC of 0.99. In the imaging data, MobileNetV2 obtained the best results, achieving an accuracy of 0.97, which is highly computationally efficient. To improve the interpretability of the models, SHAP and LIME were used to analyze the clinical feature importance, while Grad-CAM was used to analyze the discriminative regions in the CT image. The reliability of the explanations was also verified through stability analysis with Spearman rank correlation, agreement analysis with SHAP and LIME, as well as through verification with expert clinicians for the Grad-CAM visualizations. The results demonstrate that various XAI methods provide complementary insights, supporting the creation of transparent, reliable, and meaningful AI systems for lung cancer diagnosis.
Fuzzy Parameter Modeling and Survival Function Estimation for Inverse Power Maxwell Data
This paper investigates the problem of estimating the survival function for lifetimes modeled by the fuzzy inverse power Maxwell distribution, motivated by reliability and survival data subject to vagueness and imprecision in observations and parameters. The classical inverse power Maxwell model is first reviewed and its main distributional properties relevant to survival analysis are derived, including the probability density function, cumulative distribution function, survival function, and hazard rate. To capture epistemic uncertainty inherent in practical lifetime studies, the model parameters are then represented as fuzzy numbers, and the corresponding fuzzy survival function is constructed via the extension principle under appropriate α − cut formulations. Several estimation strategies for the fuzzy survival function are developed, including plug-in estimation based on maximum likelihood estimators of the crisp parameters, fuzzy least-squares-type procedures, and a possibility-based approach; explicit computational forms and algorithmic steps are provided for each method. A comprehensive Monte Carlo simulation study is conducted to compare the finite-sample behavior of the proposed estimators in terms of bias, mean squared error, and coverage characteristics across different degrees of fuzziness, sample sizes, and parameter configurations. The practical usefulness of the methodology is illustrated with a real data set from survival studies, where the inverse power Maxwell model with fuzzy parameters yields more flexible survival function estimates than its classical counterpart and better accounts for expert judgement and imprecise measurements. The results indicate that the proposed fuzzy inference framework provides a robust and informative tool for survival analysis when ambiguity in lifetime data cannot be ignored.
Copyright (c) 2026 Fatimah Abdulrazzaq Alsultan, Hind Adil Ahmed, Zakariya Yahya Algamal https://creativecommons.org/licenses/by/4.0
Addressing missing values in structural equation modeling with application to digital transformation survey data and its role in improving the quality of healthcare in Mosul
The presence of missing data is a pivotal methodological challenge across various scientific disciplines, particularly in structural equation modeling (SEM), due to its direct impact on the accuracy of statistical estimation and the quality of model fit. This study aimed to evaluate and compare the performance of three common missing data handling methods: full information (FIML), pairwise deletion (PD), and total deletion (LD), within the framework of SEM using questionnaire data on digital transformation and its role in improving the quality of sustainable healthcare in Mosul. The study employed a descriptive-analytical approach with a sample of 303 individuals. SEM analysis was performed using R software, relying on three estimation methods: maximum likelihood (ML), unweighted least squares corrected by mean and variance (ULSMV), and weighted least squares corrected by mean and variance (WLSMV). The effect of each processing method on estimation accuracy, fit quality, and statistical efficiency was evaluated. To support the applied findings, a systematic simulation study was conducted comparing the performance of pairwise and whole deletion with the ULSMV and WLSMV estimation methods under sample sizes of 200, 400, 600, 800, 1000, and 1500 and loss ratios of 0.01 and 0.1, respectively, on a four-factor model, each factor comprising five observed variables. Standardized fit indices were used for evaluation. The results showed that the FIML method outperformed most evaluation criteria in terms of statistical accuracy, fit quality, and stability. The simulation also demonstrated that pairwise deletion outperformed whole deletion at low and medium loss ratios, with relative improvement as sample size increased. ULSMV and WLSMV showed similar performance at low loss, with ULSMV tending towards better stability in smaller samples. These results underscore the need to adopt advanced methods for handling missing data and avoid exclusive reliance on traditional deletion methods in SEM applications.
Copyright (c) 2026 Safa Ahmad Jumaa, Omar Salim Ibrahim https://creativecommons.org/licenses/by/4.0
A Statistical Evaluation of Feature Selection Methods for Breast Cancer Classification: A Simulation and Real-Data Study
Breast cancer remains one of the leading causes of mortality among women worldwide, and early, accurate diagnosis is central to improving survival outcomes. This work develops a statistical evaluation framework for breast cancer classification that couples machine-learning classifiers with a family of feature selection techniques. Rather than focusing on predictive accuracy alone, we emphasise the statistical behaviour, stability, and robustness of the resulting models under varying data conditions. Five feature selection strategies—filter methods, univariate selection (Select K-Best), model-based feature importance, principal component analysis (PCA), and a hybrid score-combining scheme—are paired with nine classifiers, including logistic regression, support vector classification, random forests, gradient boosting, and XGBoost. The evaluation combines a real-data study on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset with a Monte Carlo simulation study that varies sample size, noise level, feature correlation, and the proportion of irrelevant features. In contrast to the original submission, all real-data experiments are performed with feature selection carried out strictly inside each training partition of a 50-split resampling scheme, eliminating the information leakage that produced the previously reported perfect scores. Under this leakage-free protocol, no classifier attains 100% accuracy; the strongest real-data performance is obtained by PCA-based reduction paired with logistic regression (accuracy 0.978 ± 0.005, ROC-AUC 0.994 ± 0.002), while the filter, univariate, and hybrid selectors yield identical feature subsets and are statistically indistinguishable from one another. The simulation study addresses a complementary question: the behaviour of the classifiers themselves under controlled conditions, rather than the ranking of selector–classifier pairs on the real data. Now including correlated and non linear data-generating designs, it shows that logistic regression maintains the highest mean accuracy and the highest stability across all twelve conditions, whereas ensemble methods remain competitive but slightly more variable. Differences among selectors are assessed with the Friedman test and Nemenyi and McNemar post-hoc procedures. Overall, the findings confirm that feature relevance strongly governs model variance and generalisation, and that appropriately regularised linear models remain competitive with, and often more stable than, more complex ensembles.
Copyright (c) 2026 Hanaa Elgohari, Mohamed Zakariaa Fouda , Mohamed Asim, Omar M. Elzeki https://creativecommons.org/licenses/by/4.0
An integrated heuristic-seeded adaptive large neighborhood search for transportation problems:Theoretical analysis and empirical validation
The Transportation Problem is considered to be among the fundamental optimization problems. In practice, however, it appears to be widely applied in logistics and operations research, though perhaps not in its full glory and certainly only after some reality constraints, such as limited capacity or time, are brought to the table. Exact methods provide an optimal solution; however, for larger instances, such methods may be slightly inefficient due to their computational complexity that increases exponentially. Classical heuristics are fast algorithms but the solution they provide is not usually optimal even if they are fast. Meta-heuristics require a lot of parameters and need to be finely tuned, similar to calibration. In this paper we propose the Heuristic-Seeded Adaptive Large Neighborhood Search (HS-ALNS) system, it mixes a cost sensitive greedy seeding heuristic with an adaptive large neighborhood search framework, and it kind of helps the whole process feel more responsive. The approach uses a kind of intelligent destroy and repair operators, they select what to do via self-adaptive processes and, it also lets simulated annealing take over to accept the outcomes. In the end the paper kind of frames four main contributions: (1) full ablation experiments that show how heuristic seed methods really matter, (2) larger benchmark sets, with extreme cases and pathological instances thrown in, (3) convergence proofs with a logarithmic cooling schedule that satisfies Hajek’s conditions, plus an explicit modeling of adaptive dynamics, and (4) empirical verification that supports the assumptions of Markov processes. The HS-ALNS approach gives near-optimal solutions, with better compute time, for every test problem we checked. It looks like it occurs in both the test cases studied through the benchmark instances and in a large real-world distribution example case (ten source cities, thirty destination cities). Each of our conclusions is mathematically tested, and this is a comprehensive test rather than a cursory check. This means not only that we study empirical tests for convergence but also perform proper statistical analysis with effect sizes included and multiple testing corrections. Moreover, a comparison is made between the suggested model and one of the best-performing commercial solvers available in the market, Gurobi. The benchmarking even includes problem instances of size 100×100, which tend to become quite critical. In general, all these results suggest that the framework suggested is reliable and computationally efficient; however, of course, the actual results may vary based on constraints in practice.
Copyright (c) 2026 Lamyaa Jasim Mohammed, Hind Talaat Yaseen, Edrees M. Nori Mahmood https://creativecommons.org/licenses/by/4.0
Modeling Coupled Wasting-Stunting Dynamics and Chronic Undernutrition Persistence in Indonesia
Child undernutrition remains a major global public health challenge, with wasting and stunting representing acute and chronic manifestations of nutritional failure, respectively. Although strong epidemiological associations between wasting and stunting have been widely reported, the long-term dynamical mechanisms governing their interaction at the population level remain insufficiently understood. This study develops a coupled wasting--stunting dynamical model to investigate the interaction between acute and chronic undernutrition among children under five years of age in Indonesia. The proposed framework integrates wasting progression, severe wasting dynamics, chronic stunting accumulation, and nonlinear interaction mechanisms linking acute nutritional deterioration and chronic growth failure. The model additionally incorporates low birth weight recruitment as an early-life nutritional vulnerability mechanism. Model parameters were obtained through demographic assignment, literature-informed specification, empirical approximation, and semi-mechanistic calibration using Indonesian national nutritional surveillance data covering the period 2013--2024. The analytical results revealed a hierarchical dynamical structure governed by the wasting progression threshold $\mathcal{R}_W$. The analysis demonstrated that persistent wasting exposure may amplify long-term chronic undernutrition through nonlinear interaction pathways, while chronic growth failure simultaneously increases susceptibility to recurrent wasting progression. Local stability analysis showed that the wasting subsystem remains asymptotically stable under the calibrated parameter configuration satisfying $\mathcal{R}_W<1$. Model robustness and parameter influence were further evaluated using profile likelihood analysis, bootstrap-based uncertainty assessment, local sensitivity analysis, and variance-based global sensitivity analysis using Sobol indices. The sensitivity analyses demonstrated that wasting dynamics are governed primarily by deterioration and recovery processes, whereas long-term stunting persistence is dominated mainly by baseline chronic progression mechanisms. Long-term projections under multiple intervention scenarios showed that recovery-oriented interventions substantially reduce wasting and severe wasting prevalence, but produce only relatively modest reductions in stunting prevalence over the period 2024--2045. Even under intensive intervention scenarios, the projected stunting prevalence remained above the national target threshold of 14%, indicating the presence of strong structural persistence within the chronic undernutrition subsystem. Overall, the study suggests that chronic undernutrition should not be interpreted merely as an isolated nutritional condition, but rather as an emergent population-level consequence of sustained nutritional exposure, nonlinear interaction dynamics, and long-term feedback accumulation. The proposed framework provides a quantitative basis for evaluating long-term nutritional intervention strategies in settings with limited longitudinal nutritional transition data.
Copyright (c) 2026 Nur Rahmi, Wahyuni Ekasasmita, Ahmad Fajri S., Muhammad Fadhil Nurahmad, Muhammad Rifki Nisardi, Kusnaeni https://creativecommons.org/licenses/by/4.0
A New Lindley Frailty Model with Validations, Medical Censored Applications and Value-at-Risk Analysis
This paper introduces a novel two-parameter Lindley frailty (2PLF) model designed for enhanced risk analysis and survival modeling. The proposed model incorporates an additional shape parameter, offering increased flexibility in capturing varying tail behaviors and latent heterogeneity in time-to-event data. To estimate the parameters of the 2PLF model, several methods are considered, including maximum likelihood estimation (MLE), Cram´er–von Mises (CVM), Anderson--Darling (ADE), and improved variants such as right-tail ADE (RTADE) and left-tail ADE (LEADE). These approaches are rigorously evaluated through simulation studies under different sample sizes and censoring levels to assess their performance using metrics such as bias, root mean squared error (RMSE), mean absolute deviation (Dabs), and maximum absolute deviation (Dmax). The study further investigates the application of the 2PLF model in real applications by analyzing datasets from emergency care and insurance claims, demonstrating its practical utility in risk assessment and decision-making. Risk indicators such as Value-at-Risk (VaR), Tail VaR (TVaR), and tail mean-variance are employed to evaluate the model's predictive accuracy and robustness. The results confirm that the 2PLF model outperforms traditional models, particularly in scenarios involving complex hazard patterns and high-frailty conditions. This work bridges a critical research gap by providing theoretical insights, empirical validation, and practical implementation of the 2PLF model, making it a valuable tool for researchers and practitioners in fields such as actuarial science, epidemiology, engineering, and survival analysis.
Copyright (c) 2026 Mohamed Ibrahim, Khaoula Meribout, Hafida Goual, Abdullah H. Al-Nefaie, Ahmad M. AboAlkhair, Haitham M. Yousof https://creativecommons.org/licenses/by/4.0