Narrow Results

This paper concerns the modeling of smart health sensors for data monitoring, where the data are eventually noisy, with correlated noise. In this work, we applied Clifford-based wavelets/multiwavelets for correlated noise in multi-sensors health data monitoring by estimating the set of sensor nodes minimizing an eventual error computed by the signal values at the selected nodes mostly caused by the correlated noise. Instead of directly minimizing the estimation error, we focused on evaluating a multi-level scheme based on multiwavelets for the estimation of the error between the parameter vector and its sub-vector of those nodes. Numerical simulations are provided with a comparison to some recent existing works. This model showed a performance and a fast time execution compared to those existing works. This model exceeds these models by the non-necessity to assume a priory structure of the data. Wavelets are capable to detect, localize, and eliminate the noise, even correlated, efficiently via the independent uncorrelated multiwavelets’ components.

Cystic Echinococcosis is a parasitic disease caused by the larvae of Echinococcus granulosus. The transmission of E. granulosus is affected by environmental changes and anthropogenic factors, which are in turn influenced by changes in the spatial and population dynamics of animals. The deterministic model can be extended stochastically to address the low prevalence rate, which is often observed in small mammal host populations, and to account for complex processes that reflect the highly widespread disease reservoirs and non-random mixing, such as the heterogeneous contact patterns of susceptible hosts with infectious materials. In this study, a mathematical model based on a set of differential equations that define the continuous transition between different classes was used is not regulated by host. The findings indicate that each protoscolex has an equal chance of developing into a worm and creating a dispersed population. Empirical modeling can be used to represent the frequency distribution of the number of parasites in each host using the negative binomial distribution.

Secure and private user data are more important than ever with the explosion of online gaming platforms and the resulting deluge of user information. Intending to protect gaming ecosystems and maintain user confidence, Heuristic Predictive Modeling provides a proactive security strategy by allowing early detection and mitigation of potential risks. The ever-changing nature of the game, the wide variety of user interactions, and the always-evolving strategies of cybercriminals all contribute to the singular problems that data management and security encounter in modern gaming settings. This research proposes Heuristic Predictive Modeling for Gaming Security (HPM-GS). This system can analyze gaming data in real time and detect trends and abnormalities that could indicate security breaches. It uses advanced algorithms and machine learning approaches. With HPM-GS, gaming platforms can keep their users safe and secure by anticipating and proactively addressing security threats. Several areas of gaming security can benefit from HPM-GS, such as user authentication, detection of cheats, prevention of fraud, and incident response. Enhanced user experience and platform reliability can be achieved by incorporating HPM-GS into pre-existing security frameworks, which allows gaming platforms to strengthen their defenses and efficiently reduce risks. Extensive simulation studies assess the effectiveness of HPM-GS in gaming security. The performance metrics of HPM-GS, such as detection accuracy, false positive rates, and response time, are evaluated using real-world datasets and simulated attack scenarios. The simulation findings show that HPM-GS is a good solution for protecting gaming environments from cyber-attacks. The HPM-GS is a proactive, elastic gaming application data management and security method. The purpose of this research is to emphasize the potential of HPM-GS to improve the security posture of online gaming platforms and to ensure that players have a gaming experience that is both safer and more pleasant. This is accomplished by addressing the significance of HPM-GS, potential difficulties, proposed techniques, implementations, and simulation analysis.

Cloud computing’s simulation and modeling capabilities are crucial for big data analysis in smart grid power; they are the key to finding practical insights, making the grid resilient, and improving energy management. Due to issues with data scalability and real-time analytics, advanced methods are required to extract useful information from the massive, ever-changing datasets produced by smart grids. This research proposed a Dynamic Resource Cloud-based Processing Analytics (DRC-PA), which integrates cloud-based processing and analytics with dynamic resource allocation algorithms. Computational resources must be able to adjust the changing grid circumstances, and DRC-PA ensures that big data analysis can scale as well. The DRC-PA method has several potential uses, including power grid optimization, anomaly detection, demand response, and predictive maintenance. Hence the proposed technique enables smart grids to proactively adjust to changing conditions, boosting resilience and sustainability in the energy ecosystem. A thorough simulation analysis is carried out using realistic circumstances within smart grids to confirm the usefulness of the DRC-PA approach. The methodology is described in the intangible, showing how DRC-PA is more efficient than traditional methods because it is more accurate, scalable, and responsive in real-time. In addition to resolving existing issues, the suggested method changes the face of contemporary energy systems by paving the way for innovations in grid optimization, decision assistance, and energy management.

This paper presents the development of an autopilot system for self-driving an autonomous wheeled vehicle. A mathematical model, including the power allocation system, has been designed for a vehicle with three degrees of freedom. All model parameters have been identified through experimental trials. Heading and speed controllers were designed based on Lyapunov theory. These controllers have been further fine-tuned and tested through simulations to verify their robustness against external disturbances in the system dynamics. Moreover, this work proposes guidance approaches that allow the vehicle to track desired waypoints (line of sight (LOS)), and follow a given path (cross-track error) and a predefined trajectory with obstacle avoidance. A comparative study was also proposed in this paper, wherein we evaluate the paths followed by the vehicle using distinct yaw moment control techniques which are; differential thrust controller, solely relying on a steering controller, and a combination of both. To validate the effectiveness of the proposed autopilot system, we have conducted experimental tests, specifically focusing on waypoint tracking control (LOS method). The results underscore the system’s capabilities and its potential in real-world applications.

The Thrust vector control (TVC) is a method for controlling the angular velocity and attitude of aerial vehicles (AV) by manipulating the thrust direction of propulsion. This technology enhances maneuverability and allows for dynamic aerobatics at low speeds and near-zero airspeeds without stalling at high angles of attack (AOA). As the aerodynamic control surfaces are ineffective for vehicles operating outside the atmosphere, TVC is a suitable technique for these applications. To design a control system for AVs utilizing TVC, an accurate mathematical model is essential to simulate flight parameters and optimize the control gains. This work presents a complete six degrees-of-freedom (6-DOF) high-fidelity simulation model of a thrust vector control aerial vehicle (TVC-AV). The nonlinear model is developed by dividing the mathematical representation into five submodules, including the geometrical model, the actuation model that was experimentally identified, and an aerodynamic model that was validated through semi-empirical techniques, computational fluid dynamics (CFD), and wind-tunnel experiments; in addition, the propulsion model’s characteristics are identified through experimentation, and the atmospheric model is based on International Standard Atmosphere (ISA) values. The integrated model was implemented in MATLAB^Ⓡ (Simulink) that provides a foundation for designing effective flight controllers and guidance systems.

Prevotella copri is a prominent constituent of the human gastrointestinal microbiome, and its fluctuating abundance has been linked with positive and negative influences on diseases such as Parkinson’s disease and rheumatoid arthritis. Prevotella copri demonstrates flexibility against drugs. There is presently no vaccine approved by the FDA against prevotella copri,and treatment options are restricted. Hence, this research work was designed to create an in silico-based vaccine for prevotella copri.The protein sequences of two distinct strains ofprevotella copriwere retrieved from NCBI. The T-cell and B-cell epitopes were obtained and then analyzed for antigenicity, allergenicity, docking and simulation. The peptide comprises linear B-cell and T-cell epitopes from proteins identified as potential novel vaccine candidates. The molecular dynamics (MD) simulations and protein-protein docking results revealed that the vaccine exhibits strong and Sustained interaction with Toll-like receptor 4 (TLR4). The constructed sequence was integrated into the pET-30a (+) biological vehicle (vector) for subsequent analysis expression in E. coli through the SnapGene server. The constructed multi-epitopic vaccine candidate was assessed for its structural, physicochemical and immunological properties. The results demonstrated solubility, stability, antigenicity and nonallergenicity and showed a strong affinity for its target receptors. The in silico study represents a significant step forward in designing a vaccine that could effectively eliminate Prevotella copri globally.

Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model proposed by Harik, we present a model to represent the relation between the success ratio and the PS for the GA applied in time-critical task scheduling, in which the efficiency of GAs is more necessitated than in solving other kinds of problems. Our model only needs some parameters easy to know through proper simplifications and approximations. Hence, our model is applicable. Finally, our model is verified through experiments.

Using a relatively simple model we examine how variations of the extracellular potassium concentration can give rise to synchronization of two nearby pacemaker cells. With the volume of the extracellular space and the rate of potassium diffusion as control parameters, the dual nature of this resource-mediated coupling is found to be responsible for the coexistence of competing patterns of in- and anti-phase synchronization between identical cells. Cell heterogeneity produces significant modifications of the dynamical regimes in the control parameter plane. By comparison with conventional gap junctional coupling, potassium signaling gives rise to considerable changes of the cellular response to external stimuli.

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

Traditionally, it is considered that neuronal synchronization in epilepsy is caused by a chain reaction of synaptic excitation. However, it has been shown that synchronous epileptiform activity may also arise without synaptic transmission. In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of ${\text{Na}}^{+}$, ${\text{K}}^{+}$, ${\text{Ca}}^{2+}$ and ${\text{Cl}}^{-}$ ions, glial uptake and extracellular diffusion mechanisms. We show that the network behavior with fixed ionic concentrations may be quite different from the neurons’ behavior when more detailed modeling of ionic dynamics is included. In particular, we show that in the extended model strong discharge of inhibitory interneurons may result in long lasting accumulation of extracellular ${\text{K}}^{+}$, which sustains the depolarization of the principal cells and causes their pathological discharges. This effect is not present in a reduced, purely synaptic network. These results point to the importance of nonsynaptic mechanisms in the transition to seizure.

Circuits with diverse electrical behavior are often placed in close physical proximity in order to achieve high-levels of on-chip integration. The activity of certain types of circuits can generate harmful interference, and degrade the performance of the system through electromagnetic coupling. Considerable effort in system-on-a-chip implementations is in fact related to technology and architectural considerations for minimizing this interference. This is especially the case in systems that have exacting requirements on the dynamic range such as those for wireless applications.

In this paper, we will discuss the evolution of techniques for modeling and analyzing these sources of noise generation and interference. We will provide a physical description of the problem. Techniques for extraction of electrical models to represent the media that support these noise sources will be covered. Macromodeling techniques will be discussed. Finally we will introduce the concept of functional modeling of circuit functions and present such a model for an integrated flash analog-to-digital converter.

A new modeling and parameter extraction methodology to represent the parasitic effects associated with shielded test structures is presented in this paper. This methodology allows to accurately account for the undesired effects introduced by the test fixture when measuring on-wafer devices at high frequencies. Since the proposed models are based on the physical effects associated with the structure, the obtained parameters allow the identification of the most important parasitic components, which lead to potential measurement uncertainty when characterizing high-frequency devices. The proposed methodology is applied to structures fabricated on different metal levels in order to point out the advantages and disadvantages in each case. The validity of the modeling and characterization methodology is verified by achieving excellent agreement between simulations and experimental data up to 50 GHz.

A comprehensive SPICE model is developed for single photon avalanche diodes (SPADs). The model simulates both the static and dynamic behaviors of SPADs. Parameters of the model were extracted form experimental data obtained from SPADs designed and fabricated in a standard 0.5μm CMOS process. In this model, the resistive behavior of the device was modeled with an exponential function. Moreover, the device simulated response to incident optical power stimulation is modeled. Experimentally extracted parameters were incorporated into the model, and simulation results agreed with the experimental data.

A two-dimensional numerical model was developed^1–3 to simulate the sediment and pollutant transport in a shallow basin. The developed model consist of two modules: Hydrodynamic module and sediment/pollutant transport module. A numerical hydrodynamic module based on the Saint-Venant equations, is resolved by a MacCormack numerical scheme and is used to simulate the circulation pattern in the basin. The obtained flow circulation is used as an input to the sediment/pollutant transport module to simulate the transport and dispersion of a pollutant emitted into the basin. To calibrate the numerical model, the distorted scale model of the Windermere Basin⁴ was used. In this physical model, the flow visualization and pollutant transport experiments provide a good calibration. The simulated results were found to be in good agreement with the experimental measurements and the results in Ref. 4. With the aid of the validated model, the influence of the construction of dikes on the residence time distributions in the basin was examined.

A quasi-three-dimensional mathematical model has been developed to study the morphological processes based on equilibrium sediment transport method. The flow velocities are computed by a two-dimensional horizontal depth-averaged flow model (H2D) in combination with logarithmic velocity profiles. The transport of sediment particles by a flow water has been considered in the form of bed load and suspended load. The bed load transport rate is defined as the transport of particles by rolling and saltating along the bed surface and is given by the Van Rijn relationship (1987). The equilibrium suspended load transport is described in terms of an equilibrium sediment concentration profile (ce) and a logarithmic velocity (u). Based on the equilibrium transport, the bed change rate is given by integration of the sediment mass-balance equation. The model results have been compared with a Van Rijn results (equilibrium approach) and good agreement has been found.

We simulate the interplay between productive and financial activity using a model that considers equal opportunities among individuals of a society. As the simulation evolves in time, three qualitative wealth distribution profiles are generated according to the flux of productive capital. We relate these curves to different socioeconomic structures: a primitive equalitarian society; a medieval society having a distribution of wealth with castes and discontinuities; and a modern society where this distribution is roughly exponential.

In this paper we adopt a phenomenological approach in order to develop a suitable Cellular Automata (CA) model capable to satisfactory mimic city growth and urban sprawl. The use of CA in urban expansion modeling is well known since many years, but very rarely it has been related with a down-top approach which considers inhabitants' preferences as a driving tool to characterize the CA algorithm. In addition, we consider as a control mechanism of the cell conversion rate (i.e. the number of cells experiencing conversion into urban use) the logistic function. This function is tuned on the free space (cells) at disposal for the urban development.

In this paper we present the basics of the model and we perform a simple simulation in the case of a generic geographic pattern.

In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.