Pub Date : 2025-09-13DOI: 10.1016/j.rico.2025.100613
Shahid Iqbal , Usman Riaz , Saeed Islam , Khayrilla Kurbonov , M. Ijaz Khan , Nidhal Ben Khedher
The article stated the class of -order coupled implicit Hilfer fractional differential equations associated with integral type initial conditions, which is the generalization of Riaz and Zada (2021); Albidah (2023) and other well-known models, i.e. coupled competition species model, coupled oscillation model and coupled elastic beam model having transverse vibrations. The existence solution of the proposed coupled system can be found with the help of the Laplace transform, which is the fundamental method of differential equations. Banach contraction principle and the topological degree method will be used as tools for the uniqueness and at least one solution of the considered coupled problem. Stability of the problem is one of the important issues for the real wold problem. Stability in the sense of Hyers–Ulam and its types of the proposed coupled problem can be proved if the inequality F 0. For the illustration of results, an example will be presented, and the graphs of the system and its respective perturb system clearly show the differences when the order is changing.
{"title":"Analysis of Hilfer type coupled implicit (μ,σ)-order differential equations with Riemann–Liouville fractional integrable conditions via Topological degree method","authors":"Shahid Iqbal , Usman Riaz , Saeed Islam , Khayrilla Kurbonov , M. Ijaz Khan , Nidhal Ben Khedher","doi":"10.1016/j.rico.2025.100613","DOIUrl":"10.1016/j.rico.2025.100613","url":null,"abstract":"<div><div>The article stated the class of <span><math><mrow><mo>(</mo><mi>μ</mi><mo>,</mo><mi>σ</mi><mo>)</mo></mrow></math></span>-order coupled implicit Hilfer fractional differential equations associated with integral type initial conditions, which is the generalization of Riaz and Zada (2021); Albidah (2023) and other well-known models, i.e. coupled competition species model, coupled oscillation model and coupled elastic beam model having transverse vibrations. The existence solution of the proposed coupled system can be found with the help of the Laplace transform, which is the fundamental method of differential equations. Banach contraction principle and the topological degree method will be used as tools for the uniqueness and at least one solution of the considered coupled problem. Stability of the problem is one of the important issues for the real wold problem. Stability in the sense of Hyers–Ulam and its types of the proposed coupled problem can be proved if the inequality F <span><math><mo>></mo></math></span> 0. For the illustration of results, an example will be presented, and the graphs of the system and its respective perturb system clearly show the differences when the order is changing.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100613"},"PeriodicalIF":3.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-11DOI: 10.1016/j.rico.2025.100611
Minh Tran, Nhat M. Nguyen, Tuan A. Tran
This study formulates a novel portfolio optimization framework for emerging markets through the integration of cross-validation with a multi-target shrinkage estimator (CV-MTSE). The proposed method adaptively combines the sample covariance matrix with two structured targets, the Single Index Model and the Identity Matrix. Shrinkage intensities are optimized through a grid search-based cross-validation procedure. Using Vietnamese stock market data from 2013 to 2023, we compare CV-MTSE with traditional estimators such as SCM and equal-weighted. Empirical results demonstrate that CV-MTSE consistently achieves higher risk-adjusted returns and lower volatility particularly during stable market conditions. During periods of market stress, the equal-weighted MTSE model shows stronger robustness in term of volatility. These findings contributes to the literature on covariance matrix estimation and also has practical applications in portfolio management in emerging markets.
{"title":"Enhancing portfolio optimization in emerging markets: A cross-validation multi-target shrinkage approach","authors":"Minh Tran, Nhat M. Nguyen, Tuan A. Tran","doi":"10.1016/j.rico.2025.100611","DOIUrl":"10.1016/j.rico.2025.100611","url":null,"abstract":"<div><div>This study formulates a novel portfolio optimization framework for emerging markets through the integration of cross-validation with a multi-target shrinkage estimator (CV-MTSE). The proposed method adaptively combines the sample covariance matrix with two structured targets, the Single Index Model and the Identity Matrix. Shrinkage intensities are optimized through a grid search-based cross-validation procedure. Using Vietnamese stock market data from 2013 to 2023, we compare CV-MTSE with traditional estimators such as SCM and equal-weighted. Empirical results demonstrate that CV-MTSE consistently achieves higher risk-adjusted returns and lower volatility particularly during stable market conditions. During periods of market stress, the equal-weighted MTSE model shows stronger robustness in term of volatility. These findings contributes to the literature on covariance matrix estimation and also has practical applications in portfolio management in emerging markets.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100611"},"PeriodicalIF":3.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-11DOI: 10.1016/j.rico.2025.100609
Ishaq Abdullahi Baba , Mohammed Bappah Mohammed , Kamal Bakari Jillahi , Aliyu Umar , Hasan Talib Hendi
Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. In application, the presence of anomalous observations in both predictors and responses can seriously jeopardize the prediction accuracy of the model, which in turn leads to misleading interpretations and conclusions if not correctly addressed. Furthermore, the cause of dimensionality is another serious difficulty facing many existing feature selection algorithms. To achieve more reliable feature selection and prediction accuracy, a weighted sure independence screening-based support vector machine for high-dimensional datasets is proposed. The key contribution of our proposed method is that it minimizes the influence of outliers in differentiating between significant and insignificant features and improves predictability and interpretability. Our method consists of three basic steps. In the first step, a weights-based modified reweighted fast, consistent, and high break-down point is computed. The second step utilizes the estimates of weights from the first step to select the most important variables for the model. The third step employs the support vector machine algorithm to calculate prediction values. To demonstrate the effectiveness of the developed procedure, we used both simulation and real-life data examples. Our results show that the proposed methods performs better with a clear margin compared to other procedures.
{"title":"Robust correlation feature selection based support vector machine approach for high dimensional datasets","authors":"Ishaq Abdullahi Baba , Mohammed Bappah Mohammed , Kamal Bakari Jillahi , Aliyu Umar , Hasan Talib Hendi","doi":"10.1016/j.rico.2025.100609","DOIUrl":"10.1016/j.rico.2025.100609","url":null,"abstract":"<div><div>Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. In application, the presence of anomalous observations in both predictors and responses can seriously jeopardize the prediction accuracy of the model, which in turn leads to misleading interpretations and conclusions if not correctly addressed. Furthermore, the cause of dimensionality is another serious difficulty facing many existing feature selection algorithms. To achieve more reliable feature selection and prediction accuracy, a weighted sure independence screening-based support vector machine for high-dimensional datasets is proposed. The key contribution of our proposed method is that it minimizes the influence of outliers in differentiating between significant and insignificant features and improves predictability and interpretability. Our method consists of three basic steps. In the first step, a weights-based modified reweighted fast, consistent, and high break-down point is computed. The second step utilizes the estimates of weights from the first step to select the most important variables for the model. The third step employs the support vector machine algorithm to calculate prediction values. To demonstrate the effectiveness of the developed procedure, we used both simulation and real-life data examples. Our results show that the proposed methods performs better with a clear margin compared to other procedures.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100609"},"PeriodicalIF":3.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.rico.2025.100610
Muhammad Farman , Ammara Talib , Aqeel Ahmad , Muhammad Owais Kulachi , Aceng Sambas , Mohamed Hafez
The central nervous system (CNS) is frequently affected by multiple sclerosis, a common neurological condition that can result in lesions that progress over time and space. Our work provides a mathematical model that demonstrate the course of the illness and its probability of return. A fractional order model is obtained by applying the fractal–fractional operator to a mathematical model that is designed with the notion of enhancing immune system development. To identify its stable location, a recently created system HITR is analyzed statistically and qualitatively. The study guarantees trustworthy bounded conclusions by examining the system’s well-posedness and local and global stability, which are critical characteristics of epidemic models. The Lipschitz condition is used with a fixed point theory tool to satisfy uniqueness and existence constraints. Additionally, the reproductive number is ascertained using a sensitivity study of factors including chaos control. Lyapunov first derivative functions are used to analyze the system for local and global stability in order to assess the overall impact of these measurements. By using power-law kernel at fractional orders, a dependable solution is derived by the use of the fractal–fractional operator. Furthermore, we confirm our theoretical results using numerical simulations. Our results are shown in graphs that illustrate the model’s different reactions for different values of the parameters.
{"title":"Modeling and sensitivity analysis of reaction diffusion brain disease with control rate under neurological disorder","authors":"Muhammad Farman , Ammara Talib , Aqeel Ahmad , Muhammad Owais Kulachi , Aceng Sambas , Mohamed Hafez","doi":"10.1016/j.rico.2025.100610","DOIUrl":"10.1016/j.rico.2025.100610","url":null,"abstract":"<div><div>The central nervous system (CNS) is frequently affected by multiple sclerosis, a common neurological condition that can result in lesions that progress over time and space. Our work provides a mathematical model that demonstrate the course of the illness and its probability of return. A fractional order model is obtained by applying the fractal–fractional operator to a mathematical model that is designed with the notion of enhancing immune system development. To identify its stable location, a recently created system HI<span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>v</mi></mrow></msub></math></span>TR is analyzed statistically and qualitatively. The study guarantees trustworthy bounded conclusions by examining the system’s well-posedness and local and global stability, which are critical characteristics of epidemic models. The Lipschitz condition is used with a fixed point theory tool to satisfy uniqueness and existence constraints. Additionally, the reproductive number is ascertained using a sensitivity study of factors including chaos control. Lyapunov first derivative functions are used to analyze the system for local and global stability in order to assess the overall impact of these measurements. By using power-law kernel at fractional orders, a dependable solution is derived by the use of the fractal–fractional operator. Furthermore, we confirm our theoretical results using numerical simulations. Our results are shown in graphs that illustrate the model’s different reactions for different values of the parameters.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100610"},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.rico.2025.100608
Archana Chaudhari , Masuk Abdullah , Vivek Deshpande , Tushar Zanke , Samrudhi Wath , Snehashish Mulgir , Stuti Jagtap
Lip reading, an essential yet intricate facet of communication, has seen notable progress through the application of advanced deep learning techniques. This research introduces a deep learning-based lip-reading model that integrates Conv3D layers, Multi-Head Attention mechanisms, Bidirectional LSTMs, and a Dense output layer, combined with a custom Connectionist Temporal Classification (CTC) loss function. Our comprehensive data preprocessing pipeline extracts video frames, normalizes pixel values, and converts textual alignments into numerical tokens, enabling effective model integration. The model architecture is carefully structured to capture spatiotemporal features, with Conv3D layers addressing spatial information, while Multi-Head Attention mechanisms and Bidirectional LSTMs effectively manage temporal dependencies. Residual connections and Max-Pooling layers are incorporated to enhance feature extraction and abstraction, supporting improved performance. The use of Layer Normalization and Dropout layers contributes to stable learning and mitigates overfitting. Through extensive training and evaluation, our model demonstrates a 96% accuracy rate in decoding lip movements and predicting corresponding words. The implementation of the CTC loss function allows for effective handling of variable-length sequences, further contributing to the model’s performance. This research provides a technically sound approach to lip reading, contributing to the advancement of visual speech recognition and offering potential benefits for communication accessibility among individuals with hearing impairments.
{"title":"Deep learning based 3D residual convolutional and Multi-Head Attention (3D-RMA) for lip-reading","authors":"Archana Chaudhari , Masuk Abdullah , Vivek Deshpande , Tushar Zanke , Samrudhi Wath , Snehashish Mulgir , Stuti Jagtap","doi":"10.1016/j.rico.2025.100608","DOIUrl":"10.1016/j.rico.2025.100608","url":null,"abstract":"<div><div>Lip reading, an essential yet intricate facet of communication, has seen notable progress through the application of advanced deep learning techniques. This research introduces a deep learning-based lip-reading model that integrates Conv3D layers, Multi-Head Attention mechanisms, Bidirectional LSTMs, and a Dense output layer, combined with a custom Connectionist Temporal Classification (CTC) loss function. Our comprehensive data preprocessing pipeline extracts video frames, normalizes pixel values, and converts textual alignments into numerical tokens, enabling effective model integration. The model architecture is carefully structured to capture spatiotemporal features, with Conv3D layers addressing spatial information, while Multi-Head Attention mechanisms and Bidirectional LSTMs effectively manage temporal dependencies. Residual connections and Max-Pooling layers are incorporated to enhance feature extraction and abstraction, supporting improved performance. The use of Layer Normalization and Dropout layers contributes to stable learning and mitigates overfitting. Through extensive training and evaluation, our model demonstrates a 96% accuracy rate in decoding lip movements and predicting corresponding words. The implementation of the CTC loss function allows for effective handling of variable-length sequences, further contributing to the model’s performance. This research provides a technically sound approach to lip reading, contributing to the advancement of visual speech recognition and offering potential benefits for communication accessibility among individuals with hearing impairments.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100608"},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1016/j.rico.2025.100607
Muhammad Luthfi , Agus Suryanto , Isnani Darti , Farah Aini Abdullah
In this study, we introduce a pest control model that describes the interaction among pests, natural enemies (predators), and refugia plants, incorporating intraspecific competition within the predator population. Predators interact mutually with refugia plants to maintain their presence in agricultural areas, enhancing their control of the pest population. On the other hand, heightened intraspecific competition for two key resources, nectar and pests, may encourage predators to migrate and exploit new resources. The non-negativity and boundedness of the solutions are shown to ensure that the proposed model is biologically feasible. We perform dynamic analysis to identify all potential equilibrium points and examine their local and global stability characteristics. The model has seven equilibrium points. However, only three of them are conditionally stable. The pest-free equilibrium point is stable under certain conditions, indicating that the pest population may be effectively controlled. Furthermore, we show that our system exhibits a Hopf bifurcation. Finally, we confirm our analytical results through some numerical simulations.
{"title":"Dynamics of pest control model using natural enemy and refugia plant","authors":"Muhammad Luthfi , Agus Suryanto , Isnani Darti , Farah Aini Abdullah","doi":"10.1016/j.rico.2025.100607","DOIUrl":"10.1016/j.rico.2025.100607","url":null,"abstract":"<div><div>In this study, we introduce a pest control model that describes the interaction among pests, natural enemies (predators), and refugia plants, incorporating intraspecific competition within the predator population. Predators interact mutually with refugia plants to maintain their presence in agricultural areas, enhancing their control of the pest population. On the other hand, heightened intraspecific competition for two key resources, nectar and pests, may encourage predators to migrate and exploit new resources. The non-negativity and boundedness of the solutions are shown to ensure that the proposed model is biologically feasible. We perform dynamic analysis to identify all potential equilibrium points and examine their local and global stability characteristics. The model has seven equilibrium points. However, only three of them are conditionally stable. The pest-free equilibrium point is stable under certain conditions, indicating that the pest population may be effectively controlled. Furthermore, we show that our system exhibits a Hopf bifurcation. Finally, we confirm our analytical results through some numerical simulations.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100607"},"PeriodicalIF":3.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-10DOI: 10.1016/j.rico.2025.100603
Odhiambo Francis
The study of predator-prey dynamics has long served as a cornerstone in ecological modelling. This research investigates a generalized predator-prey model incorporating a general Holling-type functional response, combined harvesting, and discrete time delays. The model considers both prey and predator harvesting strategies under realistic ecological constraints, and includes maturation and response delays to better capture biological interactions. Using qualitative analysis techniques, such as local stability, Hopf bifurcation, and numerical simulations, we explore the system's dynamic behavior. The goal of this research is to determine the optimal harvesting rate (or effort) that balances resource extraction with the need to maintain ecosystem stability and the long-term viability of both predator and prey populations. Findings from this study may inform sustainable harvesting policies and contribute to more accurate ecological forecasting models.
{"title":"Dynamical analysis of time delay effects on the stability of a harvested predator-prey system with a general Holling type response","authors":"Odhiambo Francis","doi":"10.1016/j.rico.2025.100603","DOIUrl":"10.1016/j.rico.2025.100603","url":null,"abstract":"<div><div>The study of predator-prey dynamics has long served as a cornerstone in ecological modelling. This research investigates a generalized predator-prey model incorporating a general Holling-type functional response, combined harvesting, and discrete time delays. The model considers both prey and predator harvesting strategies under realistic ecological constraints, and includes maturation and response delays to better capture biological interactions. Using qualitative analysis techniques, such as local stability, Hopf bifurcation, and numerical simulations, we explore the system's dynamic behavior. The goal of this research is to determine the optimal harvesting rate (or effort) that balances resource extraction with the need to maintain ecosystem stability and the long-term viability of both predator and prey populations. Findings from this study may inform sustainable harvesting policies and contribute to more accurate ecological forecasting models.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100603"},"PeriodicalIF":3.2,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1016/j.rico.2025.100599
Nadia Samantha Zuñiga-Peña , Salatiel Garcia-Nava , Norberto Hernandez-Romero , Juan Carlos Seck-Touh-Mora
Optimization methods like population-based algorithms are valuable when applied to multidimensional and nonlinear problems. Many engineering problems, such as controller parameterization, can be addressed using population-based algorithms since these parameters are usually found through essays, resulting in high time and resource consumption. Population-based algorithms need to define the range within which the search for the best solution is performed, known as the search space. However, due to the nonlinear nature of the systems to which these controllers are applied, there is no certainty about the search space that must be defined. This study proposes a hybrid optimization strategy that couples the Hunger Games Search (HGS) metaheuristic with an unsupervised Self Organizing Map, Kohonen Neural Network, to improve trajectory-tracking control of unmanned aerial vehicles (UAVs) transporting cable suspended loads. In the proposed NNHGS, the HGS algorithm seeks the controller gains that minimize Root Mean Square tracking Error (RMSE). At the same time, the neural network continuously reshapes the search intervals according to the evolving tracking performance. By expanding the exploration into parameter regions beyond the initial bounds, the NNHGS finds high-quality solutions that standard HGS excludes. The simulation results obtained with a Super Twisting Sliding Mode Controller (STSMC) show a reduction in the final tracking error from RMSE=0.0480 with HGS to RMSE = 0.0204 by NNHGS, along with enhanced disturbance rejection and rapid adaptation to parameter changes. These gains highlight the suitability of this method for real-world missions such as logistics, disaster relief, or remote inspection, where UAVs must remain stable under uncertain or parameter-varying conditions.
{"title":"Hybrid Hunger Games Search optimization using a neural networks approach applied to UAVs","authors":"Nadia Samantha Zuñiga-Peña , Salatiel Garcia-Nava , Norberto Hernandez-Romero , Juan Carlos Seck-Touh-Mora","doi":"10.1016/j.rico.2025.100599","DOIUrl":"10.1016/j.rico.2025.100599","url":null,"abstract":"<div><div>Optimization methods like population-based algorithms are valuable when applied to multidimensional and nonlinear problems. Many engineering problems, such as controller parameterization, can be addressed using population-based algorithms since these parameters are usually found through essays, resulting in high time and resource consumption. Population-based algorithms need to define the range within which the search for the best solution is performed, known as the search space. However, due to the nonlinear nature of the systems to which these controllers are applied, there is no certainty about the search space that must be defined. This study proposes a hybrid optimization strategy that couples the Hunger Games Search (HGS) metaheuristic with an unsupervised Self Organizing Map, Kohonen Neural Network, to improve trajectory-tracking control of unmanned aerial vehicles (UAVs) transporting cable suspended loads. In the proposed NNHGS, the HGS algorithm seeks the controller gains that minimize Root Mean Square tracking Error (RMSE). At the same time, the neural network continuously reshapes the search intervals according to the evolving tracking performance. By expanding the exploration into parameter regions beyond the initial bounds, the NNHGS finds high-quality solutions that standard HGS excludes. The simulation results obtained with a Super Twisting Sliding Mode Controller (STSMC) show a reduction in the final tracking error from RMSE=0.0480 with HGS to RMSE = 0.0204 by NNHGS, along with enhanced disturbance rejection and rapid adaptation to parameter changes. These gains highlight the suitability of this method for real-world missions such as logistics, disaster relief, or remote inspection, where UAVs must remain stable under uncertain or parameter-varying conditions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100599"},"PeriodicalIF":3.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1016/j.rico.2025.100601
Nkuba Nyerere , Verediana M. Mbalilo
Q-fever, caused by the zoonotic bacterium Coxiella burnetii, remains a significant global health concern due to its complex transmission dynamics involving livestock, humans, and the environment. This study develops a comprehensive mathematical model to investigate the spread of Q-fever and assess the effectiveness of five distinct control strategies targeting both human and animal populations. The model incorporates key epidemiological factors, including environmental contamination, which plays a critical role in sustaining indirect transmission. Numerical simulations and cost-effectiveness analysis reveal that early, coordinated, and sustained interventions are vital for effective disease control. In particular, the combination of livestock vaccination, gradual culling of seropositive animals, and public health education, emerged as the most cost-effective, achieving elimination in humans within two years, symptomatic livestock within three years, and asymptomatic livestock within four years. In the absence of interventions, the model predicts exponential disease spread, with Q-fever persisting for over six years in livestock and up to four years in humans, further fueled by environmental reservoirs. Across all scenarios, human infections are more quickly eliminated than those in livestock, highlighting the challenge of clearing environmental and animal reservoirs. These findings underscore the importance of integrated, long-term strategies that address direct and indirect transmission routes, combining animal health management, environmental decontamination, and public awareness to prevent endemicity and mitigate the health and economic burden of Q-fever.
{"title":"Optimal control and cost-effectiveness analysis of Q-fever transmission dynamics in livestock and humans","authors":"Nkuba Nyerere , Verediana M. Mbalilo","doi":"10.1016/j.rico.2025.100601","DOIUrl":"10.1016/j.rico.2025.100601","url":null,"abstract":"<div><div>Q-fever, caused by the zoonotic bacterium <em>Coxiella burnetii</em>, remains a significant global health concern due to its complex transmission dynamics involving livestock, humans, and the environment. This study develops a comprehensive mathematical model to investigate the spread of Q-fever and assess the effectiveness of five distinct control strategies targeting both human and animal populations. The model incorporates key epidemiological factors, including environmental contamination, which plays a critical role in sustaining indirect transmission. Numerical simulations and cost-effectiveness analysis reveal that early, coordinated, and sustained interventions are vital for effective disease control. In particular, the combination of livestock vaccination, gradual culling of seropositive animals, and public health education, emerged as the most cost-effective, achieving elimination in humans within two years, symptomatic livestock within three years, and asymptomatic livestock within four years. In the absence of interventions, the model predicts exponential disease spread, with Q-fever persisting for over six years in livestock and up to four years in humans, further fueled by environmental reservoirs. Across all scenarios, human infections are more quickly eliminated than those in livestock, highlighting the challenge of clearing environmental and animal reservoirs. These findings underscore the importance of integrated, long-term strategies that address direct and indirect transmission routes, combining animal health management, environmental decontamination, and public awareness to prevent endemicity and mitigate the health and economic burden of Q-fever.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100601"},"PeriodicalIF":3.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1016/j.rico.2025.100600
Md. Abdullah Bin Masud, Mostak Ahmed
We have addressed the challenge of designing robust vaccination and quarantine strategies in the presence of uncertainty and random perturbations, particularly in epidemic scenarios such as COVID-19. To realistically model the dynamics of disease spread, we develop a stochastic Susceptible–Infected–Recovered (SIR) model that incorporates both Brownian motion to capture continuous, small-scale fluctuations and Lévy jumps to represent rare events. This jump effectively captures key features of real-world epidemics, such as superspreading events, the sudden emergence of new variants, and mass gatherings, which are not captured by Poisson noise or Markov jumps. The model includes time-dependent vaccination and isolation control strategies under parameter uncertainty. We solve the optimal control problem using Pontryagin’s Maximum Principle and perform numerical simulations to assess the influence of different noise sources on infection dynamics and control performance. The results show that the incorporation of Lévy jumps significantly affects epidemic outcomes. In the case of negative Lévy jumps (representing sudden quarantine or lockdown), the maximum number of infected individuals is reduced by approximately 13.4%, and the total control cost is reduced by 31.9%. The positive jump significantly amplifies infection peaks and alters optimal control paths, underscoring its critical role in epidemic modeling. The findings highlight the need to incorporate jump-driven stochasticity when designing adaptive and resilient vaccination policies in the face of extreme and unpredictable epidemic events.
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