Results in Control and Optimization最新文献

{"title":"Corrigendum to “Fractional Optimal Control of Dark Matter Dynamics in Cosmological Models” [Results in Control and Optimization, 24 (2026) 100786]","authors":"G.M. Bahaa, A.H. Qamlo","doi":"10.1016/j.rico.2026.100807","DOIUrl":"10.1016/j.rico.2026.100807","url":null,"abstract":"","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100807"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148995329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics estimation and deep reinforcement learning based fractal inspired sliding mode control of Stewart platform","authors":"Syed M. Ali,&nbsp;Talha Rabbani,&nbsp;Muhammad Bilal,&nbsp;Ayman Alharbi,&nbsp;Fahd M. Aldosari,&nbsp;Rooh ul Amin","doi":"10.1016/j.rico.2026.100779","DOIUrl":"10.1016/j.rico.2026.100779","url":null,"abstract":"<div><div>This paper presents a novel nonlinear Stewart control method, which combines real-time dynamics estimation and an intelligent nonlinear control approach. The proposed solution combines (i) a Fractal-inspired Radial Basis Function (FRBF) network to estimate unknown system dynamics and (ii) a Deep Reinforcement Learning based Fractal-inspired Sliding Mode Controller (DRL-FSMC) to track the trajectory in uncertain conditions. The FRBF network estimates unknown disturbances with 43% parameter efficiency compared to standard RBF networks. The DRL-FSMC offers more robustness, less chattering and automatically tunes control gains online using a Soft Actor-Critic (SAC) algorithm, which enables robust trajectory tracking under parametric uncertainty and external disturbances. The reliability and accuracy of the proposed controller is proved by extensive simulations in different motion scenarios, such as step-inputs, sinusoidal paths, and uncertainty scenarios. The proposed controller exhibits better convergence rate, better tracking, and greater resistance to disturbances and modeling errors compared to the traditional PID and classical SMC approaches. The proposed approach, according to these results, has a considerable potential of applications that require agility and stability, including flight simulation and robotic-assisted surgery.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100779"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global sensitivity analysis with ANOVA-RSM for heat transfer assessment in thin needle driven flow of bio-convective Sutterby nanofluid","authors":"Suriya Vasanth,&nbsp;Naveen Padigepati","doi":"10.1016/j.rico.2026.100797","DOIUrl":"10.1016/j.rico.2026.100797","url":null,"abstract":"<div><div>The thermal transmission ability of non-Newtonian fluid plays an important role in the emerging sustainable energy and renewable resource applications, and requires careful analysis and enhancement. Therefore, the present work intends to analyze heat transfer characteristics of the Sutterby nanofluid flow over a vertical thin needle using the ANOVA-RSM approach. Also, this study examines the detailed case of Sakiadis and Blasius flow containing gyrotactic microorganisms. Additionally, the impact of Brownian movement and thermophoresis of tiny nanoparticles is cited using the Buongiorno model. The equations governing the Sutterby fluid flow were converted into a dimensionless form and solved with the bvp4c technique. The findings are presented visually, accompanied by relevant physical interpretations. Additionally, sensitivity analysis is applied to quantify the parameters pertinent to this investigation to get the desired results. Quantitative analysis of the CCD results shows that the Nusselt number can increase by approximately 283% for the Blasius flow and about 270% for the Sakiadis flow relative to the baseline case. The sensitivity analysis indicates that the Brownian motion parameter (<span><math><mrow><mi>N</mi><mi>b</mi></mrow></math></span>) and thermophoresis parameter (<span><math><mrow><mi>N</mi><mi>t</mi></mrow></math></span>) are the dominant factors, contributing approximately <span><math><mrow><mn>45</mn><mtext>%–</mtext><mn>55</mn><mtext>%</mtext></mrow></math></span> of the total output variance within the considered parameter ranges for both Blasius and Sakiadis flows. In contrast, the bioconvection parameter (<span><math><mi>λ</mi></math></span>) shows a comparatively small contribution (less than 1%), suggesting a relatively limited influence on the Nusselt number under the present modeling assumptions and parameter bounds.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100797"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning and MCDM framework for the data-informed sustainable project scheduling and material procurement problem","authors":"Mir Mostafa Majidi,&nbsp;Sadegh Sharifi,&nbsp;Mostafa Fazli Jomhour,&nbsp;Ali Heidari,&nbsp;Seyed Mohammad Hossein Orazani","doi":"10.1016/j.rico.2026.100803","DOIUrl":"10.1016/j.rico.2026.100803","url":null,"abstract":"<div><div>Construction project performance is strongly influenced by the coordination of scheduling, material procurement, and supplier selection decisions. However, these decisions are often addressed separately, despite their significant interdependence and their joint impact on project cost, completion time, and supply chain sustainability. This study proposes an integrated framework that combines machine learning (ML), multi-criteria decision-making, and multi-objective optimization to support construction project scheduling and supplier selection under material ordering decisions. To the best of our knowledge, this is the first study to employ ML within integrated project scheduling and material ordering decisions for predicting both activity duration and activity execution cost. In the first stage, ML models are developed to predict activity duration and execution cost based on historical project data, and the results show that XGBoost outperforms Random Forest, achieving <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span>values of 0.901 and 0.923, respectively. In the second stage, supplier evaluation is conducted using multiple criteria, including price, lead time, reliability, quality, capacity, and ESG performance, through a structured decision-making process. In the final stage, the predicted activity parameters and supplier information are integrated into a multi-objective optimization model to minimize total project cost and project duration while maximizing supplier ESG performance. The Augmented Epsilon Constraint method is employed to generate Pareto-optimal solutions. Results reveal clear trade-offs among objectives, demonstrating that ordering and supplier decisions vary significantly depending on managerial priorities. Overall, the proposed framework improves predictive accuracy and provides a practical decision-support tool for more integrated and sustainability-aware construction planning.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100803"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physics-Informed Neural Network-based health information system for modeling circadian gut microbiome dynamics and yogurt-driven probiotic interventions","authors":"Rooh Ali,&nbsp;Khadija Tul Kubra","doi":"10.1016/j.rico.2026.100799","DOIUrl":"10.1016/j.rico.2026.100799","url":null,"abstract":"<div><div>This study quantifies the influence of yogurt on gut microbiota dynamics in a circadian-modulated manner through the framework of data-driven health information modeling. Four key parameters for a set of six coupled nonlinear ordinary differential equations (ode’s) exhibiting sinusoidal 24-hour oscillations are specified. To solve this dynamical system and enable predictive analytics, we utilize a Physics-Informed Neural Network (PINN), which integrates machine learning and mechanical domain expertise into a single computational architecture. It is demonstrated that the solution is non-negative, unique, continuously dependent on the initial conditions, and exists locally and globally using Peano’s theorem, Picard–Lindelöf theorem, Gronwall’s inequality, and the invariance of non-negative odds. The PINN framework achieves relative <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> errors of less than <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, infers with <span><math><mrow><mn>45</mn><mo>×</mo></mrow></math></span> speedup over traditional ODE solvers and is resilient to 20% measurement noise. Factors derived from yogurt, particularly anti-inflammatory effect (<span><math><msub><mrow><mi>δ</mi></mrow><mrow><mi>Y</mi></mrow></msub></math></span>) and probiotic growth characteristics (<span><math><msub><mrow><mi>r</mi></mrow><mrow><mi>Y</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>K</mi></mrow><mrow><mi>Y</mi></mrow></msub></math></span>), have the highest effect on gut ecosystem stability. Comparative simulations show that daily yogurt intake decreases inflammation by 67.0% (Cohen’s <span><math><mrow><mi>d</mi><mo>=</mo><mo>−</mo><mn>569.90</mn></mrow></math></span>, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>130</mn></mrow></msup></mrow></math></span>) and enhances the Microbial Balance Index (MBI) by 46.4% <span><math><mrow><mo>(</mo><mi>p</mi><mo>=</mo><mn>1.2</mn><mspace></mspace><mo>×</mo><mspace></mspace><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>12</mn></mrow></msup><mo>)</mo></mrow></math></span>. The framework suggests the ideal time and dose for each chronotype to offer chrono-nutrition recommendations (morning: 0.5 KG at 06: 30; midday: 0.3 KG at 12: 00; and evening: 0.2 KG at 18: 00 and 21: 00). The interpretable, efficient, and scalable approach for knowledge discovery, predictive analytics, and decision support for personalized yogurts is nutrition and microbiome-based.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100799"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving individual credit classification through hyperparameter tuning by metaheuristics","authors":"Nguyen Anh-Tu,&nbsp;Trung D. Nguyen,&nbsp;Minh Tran","doi":"10.1016/j.rico.2026.100801","DOIUrl":"10.1016/j.rico.2026.100801","url":null,"abstract":"<div><div>Using data from 8591 individual customer profiles with demographic characteristics from a Vietnamese commercial bank, this study investigates how metaheuristic algorithms can optimize parameter settings for five machine learning models applied to classification problems. The five machine learning algorithms, Logistic Regression, Decision Tree, Random Forest, Neural Networks, and XGBoost, are investigated in two phases. Phase A classifies customer profiles with default settings, while Phase B applies metaheuristics, Simulated Annealing (SA) and Genetic Algorithm (GA), to find the optimal set of parameters by optimizing the objective function, the Recall score. The empirical results show that Phase B substantially improves Recall performance compared with Phase A, while maintaining competitive classification performance based on additional evaluation metrics. In addition, within Phase B, SA delivers significant improvements over GA for four out of five cases. The optimal algorithm in the experiments is the SA-XGBoost model that achieves a Recall score of 98.23%. The proposed managerial implications recommend leveraging the SA-XGBoost model for enhanced credit scoring and prioritizing key features like loan value and loan term to reduce principal losses.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100801"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bounded control of time-delay nonlinear PDEs via an LQR approach: Application to the generalized Kuramoto–Sivashinsky equation","authors":"R. Al Jamal,&nbsp;N. Smaoui","doi":"10.1016/j.rico.2026.100802","DOIUrl":"10.1016/j.rico.2026.100802","url":null,"abstract":"<div><div>This work develops a Linear Quadratic Regulator (LQR)-based optimal feedback controller for nonlinear partial differential equations (PDEs) subject to an input time delay. The controller is designed using the linearized dynamics and is shown to stabilize the full nonlinear system under appropriate assumptions, including the existence of a Fréchet-differentiable semigroup whose derivative coincides with the semigroup generated by the linearization. It is further established that the resulting control law is finite dimensional and provides local stabilization of the infinite dimensional solution, assuming suitable actuator conditions and a finite number of unstable eigenvalues in the linearized model. The effectiveness of the proposed approach is demonstrated on the generalized Kuramoto–Sivashinsky equation (GKS) equation, where the controller successfully stabilizes an unstable flow regime.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100802"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Framework for vector-borne disease surveillance and early warning","authors":"Prem Sankar N,&nbsp;Anita Christaline Johnvictor","doi":"10.1016/j.rico.2026.100812","DOIUrl":"10.1016/j.rico.2026.100812","url":null,"abstract":"<div><div>Vector-borne diseases, especially dengue fever, are a significant global public health issue. They account for 17% of infectious diseases and cause over 700,000 deaths each year. Current surveillance systems struggle with insufficient detail and slow response times, making it hard to predict outbreaks effectively. This project creates an AI-powered framework for predicting dengue outbreaks at a local level by combining epidemiological, meteorological, geospatial, and demographic data into a single predictive tool. The proposed framework integrates data management, disease forecasting, surveillance visualization, and AI-assisted decision support within a unified operational architecture. The data integration method aligns time-series cases from Punjab with multimodal data from Puerto Rico, such as NDVI and precipitation, along with diagnostic features. This setup allows for validation across different regions. The machine learning engine follows a three-phase approach. In Phase 1, it implements XGBoost, achieving a test R² score of 0.970 on the temporally separated Punjab forecasting dataset. In Phase 2, it develops an ensemble approach that combines XGBoost, LightGBM, and Random Forest, achieving an R² of 0.9779. Phase 3 explores LSTM-based forecasting architectures, while Transformer-based approaches are considered as potential extensions for future enhancement. An exploratory component of the framework is the integration of a Lang Chain-powered Large Language Model to provide contextual decision-support recommendations and improve interpretability of forecasting outputs. This module was functionally demonstrated and qualitatively evaluated rather than quantitatively validated. A web-based surveillance dashboard was implemented for visualization of predictions, environmental indicators, and geographic risk patterns. Validation shows strong performance, with the Phase 2 ensemble achieving the R² of 0.9779. Under the evaluated datasets and temporal validation strategy, the proposed ensemble demonstrated higher R² than several studies reported in the reviewed literature, including studies reporting R² values around 0.84 and Random Forest models reporting approximately 89% accuracy. The proposed framework has the potential to support district-level outbreak monitoring and public health decision-making. The system's design allows for future adjustments to address multiple vector-borne diseases by using cross-regional applicability. Further evaluation is required to assess large-scale deployment and operational effectiveness in real-world public health settings.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100812"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A probability-bounded approach for resilient leader–follower consensus of nonlinear multi-agents under DoS attacks","authors":"Muhammad Rehan","doi":"10.1016/j.rico.2026.100805","DOIUrl":"10.1016/j.rico.2026.100805","url":null,"abstract":"<div><div>Networked engineering systems, including robotic collectives, autonomous vehicle platoons, sensor networks, and microgrids, must maintain coordinated operation in the presence of nonlinear system dynamics and communication disruptions arising from cyber-attacks. This paper addresses the leader–follower consensus problem for nonlinear multi-agent systems (MASs) subject to stochastic denial-of-service (DoS) attacks. A directed communication topology is examined, in which the interconnection graph alternates among a finite collection of configurations because of DoS attacks. Consensus is ensured under the mild condition that the union of these switching topologies contains a directed spanning tree. Instead of assuming exact knowledge of the switching probabilities, only lower and upper bounds are applied to consider a probability-bounded methodology for stochastic DoS attacks. For agents with locally Lipschitz nonlinear dynamics on a prescribed domain, sufficient conditions ensuring exponential consensus are derived and an explicit region of exponential convergence is characterized. A dynamic nonlinear feedback protocol is developed to enhance performance against DoS-induced switching, and a decoupling-based reformulation is provided for computationally tractable convex synthesis conditions to determine the controller gains. In contrast with the existing methods, the proposed approach deals with nonlinear MASs, considers stochastic DoS attacks with only probability bounds (rather than exact switching probabilities), incorporates locally Lipschitz dynamics (rather than global Lipschitz approach), employs nonlinear dynamic controller (rather than linear static control) and guarantees exponential leader–follower consensus. A consensus control simulation example on a network of UGVs under DoS attacks by considering nonlinear friction has been provided.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100805"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soliton dynamics of the Maccari model arising in plasma physics","authors":"Humayra Dil Afroz,&nbsp;M. Zulfikar Ali,&nbsp;Mohammad Safi Ullah,&nbsp;M. Mohiuddin","doi":"10.1016/j.rico.2026.100809","DOIUrl":"10.1016/j.rico.2026.100809","url":null,"abstract":"<div><div>This paper develops some new solutions, along with trigonometric forms, Jacobi elliptic functions, and hyperbolic forms, for the Maccari system, arising in plasma physics, hydrodynamics, and nonlinear optics, applying the <span><math><msup><mrow><mi>φ</mi></mrow><mn>6</mn></msup></math></span>-model expansion method. The 3D, contour, and 2D plots clearly show kink waves, breather waves, bright, dark, and singular periodic waves, and 2D overlapping phenomena. Additionally, the stability analysis of the obtained solution and bifurcation analysis of the system in question are also described. Our results show that the applied method is a promising tool for studying wave equations with nonlinear terms and for deepening our understanding of their dynamic complexity. The results of our research may improve the understanding of the Maccari system’s nonlinear dynamical features. This research ensures that the stated method is practical and compatible with the resolution of nonlinear problems. Our research not only enhances the mathematical resources but also models intricate wave phenomena in mathematical physics and advanced materials.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"24 ","pages":"Article 100809"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148935036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}