Pub Date : 2026-03-01Epub Date: 2026-03-04DOI: 10.1016/j.rico.2026.100682
Innocent P. John , Mussa A. Stephano , Maranya M. Mayengo
Catheter-associated urinary tract infection (CAUTI) is one of the most common healthcare-associated infections, posing a significant challenge in clinical settings. This study develops a mathematical model that incorporates bacterial contamination to investigate the transmission dynamics of CAUTI. We derive the disease-free and endemic equilibria and compute the basic reproduction number, , using the next generation matrix method. The model’s well-posedness is examined through the existence and uniqueness of solutions, and the long-term behavior is analyzed to determine the stability of the equilibria. To assess the relative importance of the model parameters, we conduct a global sensitivity analysis using the extended Fourier Amplitude Sensitivity Test (eFAST) method. The results identify the catheterization rate (), catheter removal rate (), and transmission coefficients () as the most influential parameters affecting infection dynamics. These findings highlight key intervention targets for controlling CAUTI. The model also serves as a foundation for future extensions, including the incorporation of asymptomatic carriers and environmental sanitation interventions.
{"title":"Global sensitivity analysis of catheter-associated urinary tract infection models using the eFAST method","authors":"Innocent P. John , Mussa A. Stephano , Maranya M. Mayengo","doi":"10.1016/j.rico.2026.100682","DOIUrl":"10.1016/j.rico.2026.100682","url":null,"abstract":"<div><div>Catheter-associated urinary tract infection (CAUTI) is one of the most common healthcare-associated infections, posing a significant challenge in clinical settings. This study develops a mathematical model that incorporates bacterial contamination to investigate the transmission dynamics of CAUTI. We derive the disease-free and endemic equilibria and compute the basic reproduction number, <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, using the next generation matrix method. The model’s well-posedness is examined through the existence and uniqueness of solutions, and the long-term behavior is analyzed to determine the stability of the equilibria. To assess the relative importance of the model parameters, we conduct a global sensitivity analysis using the extended Fourier Amplitude Sensitivity Test (eFAST) method. The results identify the catheterization rate (<span><math><mi>ω</mi></math></span>), catheter removal rate (<span><math><mi>δ</mi></math></span>), and transmission coefficients (<span><math><mrow><mi>ϕ</mi><mo>,</mo><mspace></mspace><msub><mrow><mi>β</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><mspace></mspace><msub><mrow><mi>β</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span>) as the most influential parameters affecting infection dynamics. These findings highlight key intervention targets for controlling CAUTI. The model also serves as a foundation for future extensions, including the incorporation of asymptomatic carriers and environmental sanitation interventions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100682"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396389","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}
The present paper puts forth a state-of-the-art control strategy for a Doubly Fed Induction Generator (DFIG)-based wind energy conversion system that supplies a multi-pump irrigation network. In order to surmount the power quality limitations inherent to conventional Direct Torque Control (DTC), a Fuzzy Logic–based Improved Direct Reactive Power Control (F-IDRPC) approach has been developed. The conventional hysteresis comparators and switching table are substituted by a fuzzy inference system, a modification that results in a substantial reduction of torque, flux, and current ripples, while ensuring the maintenance of near-sinusoidal stator currents. Furthermore, a Reliability-Aware Permutation Strategy (RAPS) is integrated into the Smart Energy Management Approach (SEMA) algorithm. By replacing a single high-power pump with a modular five-pump architecture, the proposed method increases system reliability by 226% (MTBF) and equalizes mechanical wear through cyclic duty cycling. The MATLAB/Simulink simulation. The MATLAB/Simulink simulation results demonstrate a 99.15% reduction in Total Harmonic Distortion (THD), an 48.25% reduction in active power ripples, and a 55.23% reduction in local reactive power compensation ripples, with a 90.04% reduction in frequency ripples compared with conventional control. These findings substantiate the efficacy of the proposed strategy in enhancing power quality, system stability, and irrigation reliability under variable wind conditions.
{"title":"Control, reliability analysis, and intelligent energy management optimization for resilient multi-pump irrigation system powered by DFIG-based wind energy system","authors":"Salah Tamalouzt , Karim Fathi Sayeh , Kamel Djermouni , Youcef Belkhier , Abdelkrim Hamasse","doi":"10.1016/j.rico.2026.100668","DOIUrl":"10.1016/j.rico.2026.100668","url":null,"abstract":"<div><div>The present paper puts forth a state-of-the-art control strategy for a Doubly Fed Induction Generator (DFIG)-based wind energy conversion system that supplies a multi-pump irrigation network. In order to surmount the power quality limitations inherent to conventional Direct Torque Control (DTC), a Fuzzy Logic–based Improved Direct Reactive Power Control (F-IDRPC) approach has been developed. The conventional hysteresis comparators and switching table are substituted by a fuzzy inference system, a modification that results in a substantial reduction of torque, flux, and current ripples, while ensuring the maintenance of near-sinusoidal stator currents. Furthermore, a Reliability-Aware Permutation Strategy (RAPS) is integrated into the Smart Energy Management Approach (SEMA) algorithm. By replacing a single high-power pump with a modular five-pump architecture, the proposed method increases system reliability by 226% (MTBF) and equalizes mechanical wear through cyclic duty cycling. The MATLAB/Simulink simulation. The MATLAB/Simulink simulation results demonstrate a 99.15% reduction in Total Harmonic Distortion (THD), an 48.25% reduction in active power ripples, and a 55.23% reduction in local reactive power compensation ripples, with a 90.04% reduction in frequency ripples compared with conventional control. These findings substantiate the efficacy of the proposed strategy in enhancing power quality, system stability, and irrigation reliability under variable wind conditions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100668"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396202","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 : 2026-03-01Epub Date: 2026-02-07DOI: 10.1016/j.rico.2026.100671
Aqeel Ahmad , Muhammad Raza , Hijaz Ahmad , Faheem Khan , Sadia Sattar , Dragan Pamucar , Vladimir Simic
Examining the model of climate change by analyzing how changes in climate-related incidents spread within the environment, particularly in coastal areas, as a result of predictions, is the main goal of this study. Following some measurements of impact rates for various variables, a mathematical model is developed using the hypothesis of a healthy environment to investigate the rates of climate change affecting coastal communities. In addition to studying the model equilibrium points, the next generation method is used to determine the models reproductive number to climate incidents spread within the environment. To determine the most sensitive factors and look at how changes in the pace of change under various conditions affect coastal life, a sensitivity analysis was created. Both qualitative and quantitative analyses are performed on a proposed model, with particular focus on existence, boundedness, positivity, and unique solutions, which are key characteristics of the developed model. At endemic sites, the model’s local stability is confirmed both theoretically and statistically. The Lyapunov derivative by endemic point of the model is used to investigate the worldwide stability of the model. Chaos control is also used to observe the chaotic behavior of the climate change. A two-step method, Lagrange polynomials, is applied in numerical simulations to investigate the effect of the fractional operator on the generalized form of the power law kernel for ongoing surveillance of climate change under coastal lives. The simulations show how different parameters affect the changes in climate incidents spread within the environment under coastal lives. Simulations have been developed to simulate the effects and behavior of climate change brought on by both natural and human activity, as well as to implement various environmental health initiatives. This type of research will be helpful in figuring out how climate change spreads and in developing future management plans for coastal lives, based on our verified results for various strategies.
{"title":"Analysis and management of climate change incidents spread within the environment under coastal lives: Modeling and chaos control","authors":"Aqeel Ahmad , Muhammad Raza , Hijaz Ahmad , Faheem Khan , Sadia Sattar , Dragan Pamucar , Vladimir Simic","doi":"10.1016/j.rico.2026.100671","DOIUrl":"10.1016/j.rico.2026.100671","url":null,"abstract":"<div><div>Examining the model of climate change by analyzing how changes in climate-related incidents spread within the environment, particularly in coastal areas, as a result of predictions, is the main goal of this study. Following some measurements of impact rates for various variables, a mathematical model is developed using the hypothesis of a healthy environment to investigate the rates of climate change affecting coastal communities. In addition to studying the model equilibrium points, the next generation method is used to determine the models reproductive number to climate incidents spread within the environment. To determine the most sensitive factors and look at how changes in the pace of change under various conditions affect coastal life, a sensitivity analysis was created. Both qualitative and quantitative analyses are performed on a proposed model, with particular focus on existence, boundedness, positivity, and unique solutions, which are key characteristics of the developed model. At endemic sites, the model’s local stability is confirmed both theoretically and statistically. The Lyapunov derivative by endemic point of the model is used to investigate the worldwide stability of the model. Chaos control is also used to observe the chaotic behavior of the climate change. A two-step method, Lagrange polynomials, is applied in numerical simulations to investigate the effect of the fractional operator on the generalized form of the power law kernel for ongoing surveillance of climate change under coastal lives. The simulations show how different parameters affect the changes in climate incidents spread within the environment under coastal lives. Simulations have been developed to simulate the effects and behavior of climate change brought on by both natural and human activity, as well as to implement various environmental health initiatives. This type of research will be helpful in figuring out how climate change spreads and in developing future management plans for coastal lives, based on our verified results for various strategies.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100671"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396394","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 : 2026-03-01Epub Date: 2026-01-12DOI: 10.1016/j.rico.2026.100656
Ramen Ghosh
This paper develops a principled framework for incentive design in multi-agent economic systems using tools from optimal transport (OT) theory and decentralized control. We consider a class of stochastic multi-agent environments in which each agent selects actions to minimize individual cost functions that depend on both private preferences and aggregate outcomes. To promote socially desirable allocations, we introduce an OT-based mechanism design approach, where incentives are computed as gradients of a Lagrangian dual formulation over probability measures. Our main results establish: (i) a KKT-type characterization of incentive compatibility in Wasserstein space, (ii) monotonicity and fairness of equilibrium allocations under convex coupling, (iii) structural convexity of cost functionals over coupled agent dynamics, (iv) convergence of iterative market updates to optimal allocations, and (v) efficiency guarantees under decentralized feedback. We demonstrate that fairness and incentive alignment emerge naturally as solutions to constrained OT problems, allowing for scalable, interpretable, and robust economic control policies. This formulation provides a unifying perspective on decentralized optimization, mechanism design, and ergodic fairness in economic networks, and opens new directions for data-driven social planning under uncertainty.
{"title":"Optimal transport and incentive design in multi-agent economic control","authors":"Ramen Ghosh","doi":"10.1016/j.rico.2026.100656","DOIUrl":"10.1016/j.rico.2026.100656","url":null,"abstract":"<div><div>This paper develops a principled framework for incentive design in multi-agent economic systems using tools from optimal transport (OT) theory and decentralized control. We consider a class of stochastic multi-agent environments in which each agent selects actions to minimize individual cost functions that depend on both private preferences and aggregate outcomes. To promote socially desirable allocations, we introduce an OT-based mechanism design approach, where incentives are computed as gradients of a Lagrangian dual formulation over probability measures. Our main results establish: (i) a KKT-type characterization of incentive compatibility in Wasserstein space, (ii) monotonicity and fairness of equilibrium allocations under convex coupling, (iii) structural convexity of cost functionals over coupled agent dynamics, (iv) convergence of iterative market updates to optimal allocations, and (v) efficiency guarantees under decentralized feedback. We demonstrate that fairness and incentive alignment emerge naturally as solutions to constrained OT problems, allowing for scalable, interpretable, and robust economic control policies. This formulation provides a unifying perspective on decentralized optimization, mechanism design, and ergodic fairness in economic networks, and opens new directions for data-driven social planning under uncertainty.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100656"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977843","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}
Mathematical models translate real-world problems into a structured framework, which makes it easier to investigate and analyze. Multi-patch compartmental models are used to model real-world scenarios related to epidemiology. Optimal control theory is used in this model to identify cost-effective strategies to minimize the proportion of individuals infected with COVID-19 in Sri Lanka. Since a nine-patch SIR-type model is considered in this research, human dispersal behaviors play a vital role. However, due to the lack of mobility data in Sri Lanka, a gravity model approach with a modified gravity model which models the human dispersal behaviors within and between patches is used to incorporate the human dispersal behaviors into the nine-patch SIR-type model. Then, the country is divided into three clusters using K-means clustering, based on the peak number of infections in each province without any control measures, for better representation. When using the control measure effective reproduction number represents the spread of the disease with sensitivity with the current susceptible population. It is observed that, in the absence of controls, decreases from 1.55 to 1.30 within 400 days, and that it decreases from 1.57 to 0 within 20 days in the presence of controls. Control measures such as health measures and vaccination can control the disease within 40, 30–40, and 20–30 days in high-risk, moderate, and low-risk regions, respectively. Furthermore, results suggest that vaccination is the most efficient control strategy since it minimizes disturbing the lives of the general community rather than public health measures.
{"title":"Optimal control for resource allocation in a multi-patch epidemic model with gravity model-based human dispersal behavior","authors":"A.S.K. Dinasiri , A.U.S. Adikari , H.C.Y. Jayathunga , I.T.S. Piyatilake","doi":"10.1016/j.rico.2025.100648","DOIUrl":"10.1016/j.rico.2025.100648","url":null,"abstract":"<div><div>Mathematical models translate real-world problems into a structured framework, which makes it easier to investigate and analyze. Multi-patch compartmental models are used to model real-world scenarios related to epidemiology. Optimal control theory is used in this model to identify cost-effective strategies to minimize the proportion of individuals infected with COVID-19 in Sri Lanka. Since a nine-patch SIR-type model is considered in this research, human dispersal behaviors play a vital role. However, due to the lack of mobility data in Sri Lanka, a gravity model approach with a modified gravity model which models the human dispersal behaviors within and between patches is used to incorporate the human dispersal behaviors into the nine-patch SIR-type model. Then, the country is divided into three clusters using K-means clustering, based on the peak number of infections in each province without any control measures, for better representation. When using the control measure effective reproduction number <span><math><mrow><mo>(</mo><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>)</mo></mrow></math></span> represents the spread of the disease with sensitivity with the current susceptible population. It is observed that, in the absence of controls, <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> decreases from 1.55 to 1.30 within 400 days, and that it decreases from 1.57 to 0 within 20 days in the presence of controls. Control measures such as health measures and vaccination can control the disease within 40, 30–40, and 20–30 days in high-risk, moderate, and low-risk regions, respectively. Furthermore, results suggest that vaccination is the most efficient control strategy since it minimizes disturbing the lives of the general community rather than public health measures.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100648"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926712","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 : 2026-03-01Epub Date: 2026-01-03DOI: 10.1016/j.rico.2026.100654
Hayder Yousif , Zahraa Al-Milaji
Input image size for convolutional neural networks (CNNs) has played a major role in classification accuracy and network speed. Designing a large depth, scale, and resolution CNN model cannot guarantee the best performance because of the problems of overfitting and memorization. On the other hand, object detection models have produced very low performance on event-triggered camera-trap images due to highly dynamic scenes. In this paper, we propose a framework for optimizing image classification in terms of performance and complexity by selecting the convenient deep learning model for each image. Based on the image sequence activation maps, we propose Resolution Selection Model (RSM) that generates a weight value for each image in the sequence. We utilize support vector machine (SVM) and the generated weight from RSM to select the appropriate deep learning model. We utilized EfficientNet models that have different input image resolutions to classify and detect the objects from the scaled images. Our results on camera-trap and surveillance images show the efficacy of the proposed method compared to the state-of-the-art architectures in terms of accuracy and computational complexity.
{"title":"Image classification and object detection complexity optimization: Exploring deep learning models on camera trap and surveillance clips","authors":"Hayder Yousif , Zahraa Al-Milaji","doi":"10.1016/j.rico.2026.100654","DOIUrl":"10.1016/j.rico.2026.100654","url":null,"abstract":"<div><div>Input image size for convolutional neural networks (CNNs) has played a major role in classification accuracy and network speed. Designing a large depth, scale, and resolution CNN model cannot guarantee the best performance because of the problems of overfitting and memorization. On the other hand, object detection models have produced very low performance on event-triggered camera-trap images due to highly dynamic scenes. In this paper, we propose a framework for optimizing image classification in terms of performance and complexity by selecting the convenient deep learning model for each image. Based on the image sequence activation maps, we propose Resolution Selection Model (RSM) that generates a weight value for each image in the sequence. We utilize support vector machine (SVM) and the generated weight from RSM to select the appropriate deep learning model. We utilized EfficientNet models that have different input image resolutions to classify and detect the objects from the scaled images. Our results on camera-trap and surveillance images show the efficacy of the proposed method compared to the state-of-the-art architectures in terms of accuracy and computational complexity.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100654"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926632","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 : 2026-03-01Epub Date: 2025-12-28DOI: 10.1016/j.rico.2025.100652
Sepideh Hemmatian Ashrafian, Vahid Baradaran, Hamid Esmaeeli
Inappropriate water use in agriculture is one of the main challenges in water resource management in any country. This situation indicates the need to improve irrigation methods and productivity. Saving water in agriculture can have high economic value and the importance of this issue is essential in maintaining food security and preventing water crises. The main goal of current study is to optimize water consumption in the agricultural sector. For this purpose, an innovative model of the agricultural supply chain (SC) under conditions of supply and demand uncertainty is designed to simultaneously balance two conflicting goals: reducing the total SC costs and reducing water consumption. By optimizing the water supply chain and reducing costs, farmers can achieve greater productivity. This means increased income and reduced economic risks in the agricultural sector. In addition, it will help identify the balance between costs and water consumption, allowing for not only cost reduction but also the conservation of water resources in the environment. A mathematical model of the agricultural supply chain is designed according to the Jackson Queuing Network. To control the non-deterministic parameters of demand and supply, the stable box method has been used. The model is analyzed using multi-objective decision making methods such as the Torabi-Hosseini (TH) method, Enhanced Epsilon Constraint (EPC) method, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the use of high-tech processing centers leads to a significant reduction in water consumption, albeit at the cost of increased total SC expenses. As uncertainty in supply and demand rises, customer demand increases while agricultural material supply declines, prompting the expansion of processing and distribution centers. Furthermore, increasing the stability factor of the model improves water efficiency and demand fulfillment but leads to higher overall costs. Balancing sustainability and cost-efficiency in agricultural supply chain requires managing uncertainty through advanced modeling and technology investment.
{"title":"A novel water bi-objective optimization in agricultural supply chains using Jackson Queue Network","authors":"Sepideh Hemmatian Ashrafian, Vahid Baradaran, Hamid Esmaeeli","doi":"10.1016/j.rico.2025.100652","DOIUrl":"10.1016/j.rico.2025.100652","url":null,"abstract":"<div><div>Inappropriate water use in agriculture is one of the main challenges in water resource management in any country. This situation indicates the need to improve irrigation methods and productivity. Saving water in agriculture can have high economic value and the importance of this issue is essential in maintaining food security and preventing water crises. The main goal of current study is to optimize water consumption in the agricultural sector. For this purpose, an innovative model of the agricultural supply chain (SC) under conditions of supply and demand uncertainty is designed to simultaneously balance two conflicting goals: reducing the total SC costs and reducing water consumption. By optimizing the water supply chain and reducing costs, farmers can achieve greater productivity. This means increased income and reduced economic risks in the agricultural sector. In addition, it will help identify the balance between costs and water consumption, allowing for not only cost reduction but also the conservation of water resources in the environment. A mathematical model of the agricultural supply chain is designed according to the Jackson Queuing Network. To control the non-deterministic parameters of demand and supply, the stable box method has been used. The model is analyzed using multi-objective decision making methods such as the Torabi-Hosseini (TH) method, Enhanced Epsilon Constraint (EPC) method, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the use of high-tech processing centers leads to a significant reduction in water consumption, albeit at the cost of increased total SC expenses. As uncertainty in supply and demand rises, customer demand increases while agricultural material supply declines, prompting the expansion of processing and distribution centers. Furthermore, increasing the stability factor of the model improves water efficiency and demand fulfillment but leads to higher overall costs. Balancing sustainability and cost-efficiency in agricultural supply chain requires managing uncertainty through advanced modeling and technology investment.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100652"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173471","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 : 2026-03-01Epub Date: 2026-01-14DOI: 10.1016/j.rico.2026.100658
M.S. Rahman , Rehena Nasrin , M.H.A. Biswas
Encephalitis is an acute inflammatory disease of the brain and continues to pose a significant public health challenge, particularly in the case of viral infections capable of sustained human-to-human transmission and progression to severe clinical outcomes. Effective disease management requires intervention strategies that can reduce transmission while avoiding excessive strain on limited healthcare resources. In this study, we develop and analyze an SEITR-type compartmental model that incorporates multiple intervention measures, including prevention, early treatment, intermittent therapy, and suppressive treatment. To better capture disease severity and healthcare demand, additional compartments representing intensive care unit (ICU) admission and ventilator support are included. Numerical simulations are carried out to investigate the combined impact of these interventions on disease dynamics and associated costs. The results indicate that coordinated implementation of control measures can substantially reduce the epidemic burden, lowering the peak number of infections by approximately 85 % and cumulative cases by about 95 % compared with an uncontrolled scenario, while remaining economically feasible within the model assumptions. These findings highlight the potential benefits of integrated intervention strategies for mitigating transmission and managing healthcare capacity during encephalitis outbreaks. The proposed framework provides a quantitative basis for comparative assessment of control strategies and may serve as a decision-support tool for exploring intervention trade-offs in the context of viral encephalitis.
{"title":"Strategic intervention policies of human-to-human viral encephalitis: a mathematical control approach","authors":"M.S. Rahman , Rehena Nasrin , M.H.A. Biswas","doi":"10.1016/j.rico.2026.100658","DOIUrl":"10.1016/j.rico.2026.100658","url":null,"abstract":"<div><div>Encephalitis is an acute inflammatory disease of the brain and continues to pose a significant public health challenge, particularly in the case of viral infections capable of sustained human-to-human transmission and progression to severe clinical outcomes. Effective disease management requires intervention strategies that can reduce transmission while avoiding excessive strain on limited healthcare resources. In this study, we develop and analyze an SEITR-type compartmental model that incorporates multiple intervention measures, including prevention, early treatment, intermittent therapy, and suppressive treatment. To better capture disease severity and healthcare demand, additional compartments representing intensive care unit (ICU) admission and ventilator support are included. Numerical simulations are carried out to investigate the combined impact of these interventions on disease dynamics and associated costs. The results indicate that coordinated implementation of control measures can substantially reduce the epidemic burden, lowering the peak number of infections by approximately 85 % and cumulative cases by about 95 % compared with an uncontrolled scenario, while remaining economically feasible within the model assumptions. These findings highlight the potential benefits of integrated intervention strategies for mitigating transmission and managing healthcare capacity during encephalitis outbreaks. The proposed framework provides a quantitative basis for comparative assessment of control strategies and may serve as a decision-support tool for exploring intervention trade-offs in the context of viral encephalitis.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"22 ","pages":"Article 100658"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173541","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 : 2025-12-01Epub 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":"2025-12-01","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 : 2025-12-01Epub Date: 2025-11-10DOI: 10.1016/j.rico.2025.100632
A. Agathiyan , Vinothkumar. B , Ali Akgul , Fahad Sameer Alshammari
Chaotic behavior in financial systems strongly influences investment strategies, risk management, and policy decisions. Conventional fractional calculus, however, has limitations in capturing the memory and scaling effects that characterize such complexity. To address this gap, the present study employs a novel differential operator that unifies fractal and fractional calculus through the Caputo and Atangana–Baleanu kernels. The objective is to investigate the nonlinear dynamics of a financial chaotic model using fractal–fractional derivative operators. A numerical scheme is implemented to generate system trajectories, and the Lyapunov exponent is applied to assess chaotic transitions. The results show that variations in saving rate, per-investment cost, and demand elasticity significantly affect system stability and regime shifts. Compared with classical fractional formulations, the proposed approach uncovers crossover phenomena in phase portraits and reveals novel attractor structures. These findings provide deeper insight into the mechanisms underlying financial complexity and demonstrate the effectiveness of fractal–fractional calculus as a powerful framework for modeling real-world economic dynamics.
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