Results in Control and Optimization最新文献

This study presents a mathematical model to investigate the patterns of transmission in lymphatic filariasis. The model considers chronic, acute, and asymptomatic individuals and integrates key control strategies. Random synthetic data is generated robustly through numerical solutions to closely replicate real-world scenarios and encompass uncertainties. The synthetic data adheres to a Gaussian distribution to ensure validity and reliability. Following the derivation of the basic and effective reproduction number using the next generation matrix approach, Latin Hypercube Sampling (LHS) and the Partial Rank Correlation Coefficient (PRCC) algorithm is utilized to assess the parameters that significantly influence the model outputs. The study examine the trajectories of different population compartments through numerical simulations over time, with particular emphasis on the role played by asymptomatic individuals in the transmission of the disease. To assess the potential for disease elimination, the study introduces a range of strategies involving protective measures, treatment interventions, and mosquito control. These strategies are determined through sensitivity analysis. The findings demonstrate that the simultaneous implementation of all control measures has a noteworthy effect in managing lymphatic filariasis. In conclusion, the proposed model enhances understanding of lymphatic filariasis dynamics and informs effective control strategies.

Prey–predator interaction is among the most crucial aspects of ecology. In this paper, the dynamics of a prey–predator system in which the prey and predator populations experience a birth rate reduction due to global warming and the wind flow effect on them are investigated. The system’s well-posedness before presenting findings about the existence of potential equilibrium points is studied. Conditions under which a different number of interior equilibria exist are also derived here. It is very important to know that when the positive equilibrium point is stable, this reflects positively on the ecosystem, its continuity and persistence. The conditions that have been identified are conditions that preserve the ecosystem from extinction when environmental influences such as global warming and wind speed are present. In addition, the local stability study around all equilibrium points and established the transversality criteria for the occurrence of transcritical bifurcations and Hopf bifurcations is conducted. The existence of different equilibrium points numerically by altering the prey’s global warming effect is illustrated , also showcasing the model’s dynamic character from a mathematical perspective. For significant amounts of global warming, its notice that the system exhibits periodic behavior. Thus, ecologists can approximate the parameters necessary to investigate and obtain such crucial findings regarding nonlinear systems with the aid of the current study.

In this paper, the modeling of the network attacks of cloud computing through Graph Neural Networks is considered. Based on structural features and relationships between neighboring nodes and the edges of the cloud ecosystem a cyberattack detection method is proposed. A simulation dataset is created on the CSE-CIC-IDS2018 dataset to train and test the proposed graph neural network based models. In a comparative analysis of the suggested method with the existing one superior results are obtained from the model constructed on the GraphSAGE algorithm. Thus in the recognition of dataset samples, the model obtained a value of 0.97739 according to the accuracy metric. The values obtained by the algorithm on precision, recall, and F1-score metrics were also higher compared to the Graph Convolutional Neural Network model.

In this article, we analyse the controllability criteria on sobolev-type Volterra-Fredholm functional Integro-differential Equations (SVFIDE) via Atangana–Baleanu Caputo (ABC) derivative. The primary outcomes was established by utlising the concepts on the measure of noncompactness, semigroup theory, the contraction principle and Darbo fixed point technique with nonlocal condition. An illustrative example to demonstrate the analytical result is provided at the end.

Ranking fuzzy values is often essential in fuzzy multi-criteria decision-making (FMCDM) systems since it is established on the comparison of hazy data pairs, even though the comparison needs temporal complexity. Then to provide a fuzzy MCDM approach under min–max operations for avoiding the fuzzy comparison. By using fuzzy TOPSIS’s min and max operations, the suggested method produces two positive and negative ideal solutions of each alternative. Here, before discussing the effectiveness of the matrices between the alternatives, first to analyze the weighted strength and weakness indices from both positive and negative indices. The results of the method have been described using a straightforward case study of a proper location for creating a college. The best location can be identified using this method based on various criteria. The outcomes are also compared with the suggested method for its effectiveness. Additionally, this problem analyzes the fuzzy TOPSIS approach in an interval-valued number for greater efficiency.

This article deals with an eco-epidemiological predator–prey model system with Allee effect on prey species and different functional responses. The growth function of the prey species is considered to follow Allee effect as this effect has an important and fundamental aspect on population growth. In this study we explore stability behaviour of the system along with bifurcation properties. It is also shown that the system experiences chaotic behaviour via period-doubling bifurcation as the parameters pass through some threshold values. The study’s validity is authenticated through numerical simulations using appropriate tools. The numerical simulations reveal that the system exhibits diverse dynamic behaviours with slight variations in system parameters.

Infectious diseases have become a severe concern in our society in recent times. In this respect, conducting a qualitative investigation of infectious disease systems is very crucial. Nowadays, developing a mathematical model and its analysis for disease control has been very popular. Our present study has formulated a SIR-type epidemic model by considering a saturated incidence function with media impact. To account for the influence of memory, we have modified the model by employing a system of fractional-order differential equations of Caputo’s notion. We have determined all the system equilibrium points and discussed their stability criteria based on the basic reproduction number ($R_0$) as the threshold parameter. It is observed that for the threshold $R_0<1$, disease-free equilibrium becomes both locally and globally asymptotically stable. A transcritical bifurcation occurs at the threshold $R_0=1$. Further, as the threshold $R_0$ exceeds unity, the endemic equilibrium becomes asymptotically stable. A fractional-order optimal control problem with treatment as the control parameter is developed to reduce the impact of disease. Using Pontryagin’s Maximum Principle yields a theoretical and numerical solution to the fractional order optimal control problem. It is observed that both the media and memory effect dramatically reduces infection rates. Considering environmental variations, the stochastic stability of the endemic equilibrium point has also been studied. Sensitivity analysis is carried out to comprehend how the parameters impact the threshold value ($R_0$). Finally, extensive numerical simulations are performed to demonstrate our theoretical examination.

The insertion of renewable energy sources into the grid, as well as the development of power electronics technology to regulate loads that are not linear, had an impact on power quality (PQ). This study focuses on the PQ enhancement of grid-connected and standalone solar PV systems (SPVS) with battery energy storage device (BESD) for the Electric vehicle (EV) charging station (EVCS) load in addition to the local load. Here, a hybrid control strategy that uses both the superior qualities of the sliding mode controller (SMC) and the fuzzy logic controller (FLC) is suggested for the unified power quality conditioner (UPQC's) shunt filter. It's major goal is to achieve steady DC capacitance voltage (SVDC) during load (like EVCS, non linear balanced/unbalanced etc.) and irradiation variations, diminish of total harmonic distortion (THD) in source current and load voltage, and mitigate sag, swell disturbances, and source voltage unbalances. The created model's performance is assessed using two scenarios (grid and island) under four case studies with varying combinations of loads and grid voltage circumstances. However, to establish the superiority of the proposed technology, comparative research with standard technologies such as proportional integral controller (PIC) and SMC controllers is required. THD is reduced by the proposed method to 2.25 %, 2.36 %, and 1.71 %, It is inferior to the current approaches found in the survey.

The Gradient Descent (GD) paradigm is a foundational principle of modern optimization algorithms. The GD algorithm and its variants, including accelerated optimization algorithms, geodesic optimization, natural gradient, and contraction-based optimization, to name a few, are used in machine learning and the system and control domain. Here, we proposed a new algorithm based on the control theoretical perspective, labeled as the Controlled Gradient Descent (CGD). Specifically, this approach overcomes the challenges of the abovementioned algorithms, which rely on the choice of a suitable geometric structure, particularly in machine learning. The proposed CGD approach visualizes the optimization as a Manifold Stabilization Problem (MSP) through the notion of an invariant manifold and its attractivity. The CGD approach leads to an exponential contraction of trajectories under the influence of a pseudo-Riemannian metric generated through the control procedure as an additional outcome. The efficacy of the CGD is demonstrated with various test objective functions like the benchmark Rosenbrock function, objective function with a lack of flatness, and semi-contracting objective functions often encountered in machine learning applications.

This paper introduces a mathematical model that simulates the transmission of the dengue virus in a population over time. The model takes into account aspects such as delays in transmission, the impact of inhibitory effects, the loss of immunity, and the presence of partial immunity. The model has been verified to ensure the positivity and boundedness. The basic reproduction number $R_0$ of the model is derived using the advanced next-generation matrix approach. An analysis is conducted on the stability criteria of the model, and equilibrium points are investigated. Under appropriate circumstances, it was shown that there is local stability in both the virus-free equilibrium and the endemic equilibrium points when there is a delay. Analyzing the global asymptotic stability of equilibrium points is done by using the appropriate Lyapunov function. In addition, the model exhibits a backward bifurcation, in which the virus-free equilibrium coexists with a stable endemic equilibrium. By using a sensitivity analysis technique, it has been shown that some factors have a substantial influence on the behavior of the model. The research adeptly elucidates the ramifications of its results by effortlessly validating theoretical concepts with numerical examples and simulations. Furthermore, our research revealed that augmenting the rate of inhibition on infected vectors and people leads to a reduction in the equilibrium point, suggesting the presence of an endemic state.