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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 a unified approach for incorporating free-form solids in bilateral Brep and CSG representation schemes, by resorting to low-degree (quadratic, cubic) algebraic surface patches. We develop a general CSG solution that represents a free-form solid as a boolean combination of a direct term and a complicated delta term. This solution gives rise to the trunctet-subshell conditions, under which the delta term computation can be obviated. We use polyhedral smoothing to construct a Brep consisting of quadratic algebraic patches that meet with tangent-plane continuity, such that the trunctet-subshell conditions are guaranteed automatically. This guarantee is not currently available for cubic patches. The general CSG solution thus applies whenever trunctet-subshell conditions are violated, e.g. sometimes for cubic patches or sometimes for patches of any degree that are subject to shape control operations. Manifold solids of arbitrary topology can be represented in our dual representation system. Ensuing CSG constructs are parallel processed on the RayCasting Engine to support a wide range of solid modeling applications, including general sweeping, Minkowski operations, NC machining, and touch-sense probing.

In this short note, we discuss the basic approach to computational modeling of dynamical systems. If a dynamical system contains multiple time scales, ranging from very fast to slow, computational solution of the dynamical system can be very costly. By resolving the fast time scales in a short time simulation, a model for the effect of the small time scale variation on large time scales can be determined, making solution possible on a long time interval. This process of computational modeling can be completely automated. Two examples are presented, including a simple model problem oscillating at a time scale of 10^–9 computed over the time interval [0,100], and a lattice consisting of large and small point masses.

The aim of this paper is to discuss biological and computational models of tumor-immune system interactions. To this end we provid first a short introduction to the field of general immunology, then a more in-depth exposition of cancer immunology. Finally we discuss a first approach to vaccine that prevent tumor onset from a biological point of view and we describe how to reproduce this phenomenon from a computational model.

The paper provides a broad discussion of multiscale and structural features of sheared turbulent flows. Basic phenomenological aspects of turbulence are first introduced, largely in descriptive terms with particular emphasis placed on the range of scales encountered in turbulent flows and in the identification of characteristic scale ranges. There follows a discussion of essential aspects of computational modeling and simulation of turbulence. Finally, the results of simulations for two groups of flows are discussed. These combine shear, separation, and periodicity, the last feature provoked by either a natural instability or by unsteady external forcing. The particular choice of examples is intended to illustrate the capabilities of such simulations to resolve the multiscale nature of complex turbulent flows, as well as the challenges encountered.

Structural hierarchies are universal design paradigms of biological materials, e.g., several materials in nature used for carrying mechanical load or impact protection such as bone, nacre, dentin show structural design at multiple length scales from the nanoscale to the macroscale. Another example is the case of diatoms, microscopic mineralized algae with intricately patterned silica-based exoskeletons, with substructure from the nanometer to micrometer length scale. Previous studies on silica nano-honeycomb structures inspired from these diatom substructures at the nanoscale have shown a great improvement in plasticity, ductility and toughness through these designs over macroscopic silica, though along with a substantial reduction in stiffness. Here, we extend the study of these structural designs to the micron length scale by introducing additional hierarchy levels to implement a multilevel composite design. To facilitate our computational experiments we first develop a mesoscale particle-spring model description of the mechanics of bulk silica/nano-honeycomb silica composites. Our mesoscale description is directly derived from constitutive material behavior found through atomistic simulations at the nanoscale with the first principles-based ReaxFF force field, but is capable of describing deformation and failure of silica materials at tens of micrometer length scales. We create several models of randomly-dispersed fiber-composite materials with a small volume fraction of the nano-honeycomb phase, and analyze the fracture mechanics using J-integral and R-curve studies. Our simulations show a dominance of quasi-brittle fracture behavior in all cases considered. For particular materials with a small volume fraction of the nano-honeycomb phase dispersed as fibers within a bulk silica matrix, we find a large improvement (≈4.4 times) in toughness over bulk silica, while retaining the high stiffness (to 70%) of the material. The increase in toughness is observed to arise primarily from crack path deflection and crack bridging by the nano-honeycomb fibers. The first structural hierarchy at the nanometer scale (nano-honeycomb silica) provides large improvements in ductility and toughness at the cost of a large reduction in stiffness. The second structural hierarchy at the micron length scale (bulk silica/nano-honeycomb composite) recovers the stiffness of bulk silica while substantially improving its toughness. The results reported here provide direct evidence that structural hierarchies present a powerful design paradigm to obtain heightened levels of stiffness and toughness from multiscale engineering a single brittle — and by itself a functionally inferior material — without the need to introduce organic (e.g., protein) phases. Our model sets the stage for the direct simulation of multiple hierarchical levels to describe deformation and failure of complex biological composites.

In this paper, by using the system potential of two bubbles and with a special interest in the interaction by exchange of volume and without exchange of mass, a system of equations governing the evolution of two bubbles is proposed. This two-bubble model shows terms that do not appear in the models of interaction between bubbles. The two-bubble model is compared with the modified Rayleigh–Plesset equation and a validation with the experimental study of Ohl [2002] is presented. The numerical results show that, on one hand, the development of small nearby bubbles can slow down the evolution of the biggest local one, while their disappearance can favor its growing. Furthermore, in the case of two bubbles in particular, the small bubble exchanges volume with the big one during their evolutions. On the other hand, contrary to the modified Rayleigh–Plesset model, the two-bubble model predicts appearance and disappearance of small bubbles in the neighborhood of the big bubble as it is observed in the experimental study of Ohl [2002].

The present findings show in particular that the interaction by exchange of volume can be very important in the cavitation born phase and it is necessary to take into account the interaction between bubbles as well as the disappearance of small ones on the evolution of the biggest local bubble. Also, this two-bubble model predicts an exchange of volume between both bubbles equal to zero when they are perfectly identical.

This paper proposes a theoretical model for the description of tension-compression cyclic plasticity of gradient nanostructured (GNS) metals. The gradient grain size effect is considered by introducing the Hall–Petch relation for local yield stress and strain hardening. With the experimentally measured grain size distribution profile, the average axial stress can be calculated for cylindrical GNS metal specimens. The model was verified using experimental data obtained from 316L stainless steel treated by surface mechanical rolling treatment (SMRT). Moreover, the corresponding strain energy for cyclic plasticity can be calculated from the constitutive equations, providing an energy-based approach to explain the fatigue life of gradient 316L stainless steel.

The available potential plant waste could be worthy material to strengthen polymers to make sustainable products and structural components. Therefore, modeling the natural fiber polymeric-based composites is currently required to reveal the mechanical performance of such polymeric green composites for various green products. This work numerically investigates the effect of various fiber types, fiber loading, and reinforcement conditions with different polymer matrices towards predicting the mechanical performance of such natural fiber composites. Cantilever beam and compression schemes were considered as two different mechanical loading conditions for structural applications of such composite materials. Finite element analysis was conducted to modeling the natural fiber composite materials. The interaction between the fibers and the matrices was considered as an interfacial friction force and was determined from experimental work by the pull out technique for each polymer and fiber type. Both polypropylene and polyethylene were considered as composite matrices. Olive and lemon leaf fibers were considered as reinforcements. Results have revealed that the deflection resistance of the natural fiber composites in cantilever beam was enhanced for several reinforcement conditions. The fiber reinforcement was capable of enhancing the mechanical performance of the polymers and was the best in case of 20 wt.% polypropylene/lemon composites due to better stress transfer within the composite. However, the 40 wt.% case was the worst in enhancing the mechanical performance in both cantilever beam and compression cases. The 30 wt.% of polyethylene/olive fiber was the best in reducing the deflection of the cantilever beam case. The prediction of mechanical performance of natural fiber composites via proper numerical analysis would enhance the process of selecting the appropriate polymer and fiber types. It can contribute finding the proper reinforcement conditions to enhance the mechanical performance of the natural fiber composites to expand their reliable implementations in more industrial applications.

In this paper, we present a competition model of malignant tumor growth that includes the immune system response. The model considers two populations: immune system (effector cells) and population of tumor (tumor cells). Ordinary differential equations are used to model the system to take into account the delay of the immune response. The existence of positive solutions of the model (with/without delay) is showed. We analyze the stability of the possible steady states with respect to time delay and the existence of positive solutions of the model (with and without delay). We show theoretically and through numerical simulations that periodic oscillations may arise through Hopf bifurcation. An algorithm for determining the stability of bifurcating periodic solutions is proved.

The paper addresses the analysis of nonlinear dynamical models of some microbial growth processes. Equilibrium points, stability analysis, and structural properties are studied for different bioprocesses with various kinetics structures. First, a simple micro-organism growth process on a single limiting substrate is widely analyzed. Second, a microbial growth process combined with an enzyme-catalyzed reaction is investigated. The analysis shows that these kinds of bioprocesses have multiple equilibria, stable or unstable, operational or non-operational. The partition of nonlinear model in linear and nonlinear parts via some structural properties leads to kinetic decoupling and facilitates the equilibria and stability analysis. The performed research is useful for model reduction and for the design of observers and control algorithms. To illustrate the study results, several numerical simulations are provided.

Maximum probability of existence of cancer in human bodies is normally diagnosed very late, so that it is highly cumbersome for physicians to cure. Reliability in predicting cancer at initial stage is always needed, so that curing and medical recovery is possible. In this paper, an investigation was made to diagnose the presence of blood cancer using Markov Chain Monte Carlo (MCMC) trace model, which is most efficient on a wide range of complex Bayesian statistical models. The analysis was carried out using version 18 of SPSS AMOS software. Totally, 19 components were considered from the blood samples of 750 patients. Various factors such as class, age, lymphatics, block of affarc, block of lymph c, block of lymph s, bypass, extravasate, regeneration of, early uptake in, lym nodes dimin, lym nodes enlar, change in lym, defect in node, changes in node, changes in strue special forms, dislocation, exclusion of node, number of nodes in blood cancer are analyzed. The maximum likelihood estimators of the parameters were derived and assessed their performance through a Monte Carlo simulation study. The convergence in prior distribution and posterior distribution takes irregular position in the diagrams and thus blood cancer is diagnosed through this model.

Pathogenic bacteria in human system mature through the bio-synthesis of protective layer known as cell wall. This bacterial cell wall growth occurs in the presence of enzyme released by it. After maturation by the cell wall formation, pathogenic bacteria become harmful for human body as they are responsible for different diseases. Antibiotics or drugs are employed for curing bacterial diseases through the inhibition of this maturation process and it occurs by the binding progression of antibiotics with the released enzyme. But nowadays, drugs or antibiotics like $\beta$-lactum family (Amoxcillin) which are generally used for inhibition of bio-synthesis of cell wall become ineffective due to evolution of antibiotic resistance by the bacteria. Antibiotic resistance occurs when an antibiotic has lost its ability to effectively control or kill bacterial growth. As a result, the bacteria becomes “resistant” and continue to multiply for the generation of robust pathogenic bacteria in spite of drug administration. This is due to the release of another type of enzyme by the resistant bacteria which binds with the active antibiotic or drug making it ineffective. Hence, another type of drug (Clauvanic acid) is combined to resist the activity of drug hydrolyzing enzyme so that the initial drug can act effectively. Hence a combination of drug therapy is applied to cure the bacterial diseases successfully. We developed a mathematical model based on the bacterial enzyme and bacterial cell wall proliferation mechanism and showed how we can reduce the bacterial infection in the resistant cases with application of combination drugs (Amoxcillin and Clauvanic acid) to restore normal health. Based on the enzymatic activity and individual drug dynamics we studied the overall system under the single drug and combinational drug administration through our formulated model analysis. We also demonstrated the different dosing time interval and dosing concentration to evaluate the optimized drug administration for arresting the cell wall formation completely. Sensitivity of the different kinetic rate constant also has been performed with subject to drug hydrolyzing enzyme. Our analytical and numerical studies also confirm the efficiency of the combinational drug treatment compared to single drug treatment being more effective in drug resistant cases providing recovery from bacterial disease.

A model of the growth curve of microorganisms was proposed, which reveals a relationship with the number of a ‘golden section’, 1.618…, for main parameters of the growth curves. The treatment mainly concerns the ratio of the maximum asymptotic value of biomass in the phase of slow growth to the real value of biomass accumulation at the end of exponential growth, which is equal to the square of the ‘golden section’, i.e., 2.618. There are a few relevant theorems to explain these facts. New, yet simpler, methods were considered for determining the model parameters based on hyperbolic functions. A comparison was made with one of the alternative models to demonstrate the advantage of the proposed model. The proposed model should be useful to apply at various stages of fermentation in scientific and industrial units. Further, the model could give a new impetus to the development of new mathematical knowledge regarding the algebra of the ‘golden section’ as a whole, as well as in connection with the introduction of a new equation at decomposing of any roots with any degrees for differences between constants and/or variables.

Vector-borne diseases can usually be examined with a vector–host model like the $SIRUV$ model. This, however, depends on parameters that contain detailed information about the mosquito population that we usually do not know. For this reason, in this article, we reduce the $SIRUV$ model to an $SIR$ model with a time-dependent and periodic transmission rate $\beta (t)$. Since the living conditions of the mosquitos depend on the local weather conditions, meteorological data sets flow into the model in order to achieve a more realistic behavior. The developed $SIR$ model is adapted to existing data sets of hospitalized dengue cases in Jakarta (Indonesia) and Colombo (Sri Lanka) using numerical optimization based on Pontryagin’s maximum principle. A previous data analysis shows that the results of this parameter fit are within a realistic range and thus allow further investigations. Based on this, various simulations are carried out and the prediction quality of the model is examined.

This paper deals with a discrete-time dynamical system generated by a modified susceptible–infected–recovered–dead model (SIRD model; nonlinear operator) in three-dimensional simplex. We introduce a novel approach that incorporates the SIRD model with the quadratic stochastic operator (QSO) that allows for real-time forecasting. The basic reproductive number $R_0$ is obtained. We describe the set of fixed points of the operator and demonstrate that all fixed points are non-hyperbolic. Further, we study the asymptotical behavior of the trajectories of this system and show that SIRD operators have a regularity property.

Dynamic modeling and simulation of underwater robotic vehicles is essential for control. In this paper, systematic dynamic modeling and simulation of a remotely operated vehicle (ROV) is developed and named as ROV Design and Analyses (RDA). For the initial design, hydrodynamic coefficients used in the ROV dynamic model were estimated using computational fluid dynamic software and experiments. The proposed RDA toolbox written in MATLAB™ and Simulink™ platform provides a means to simulate the mathematical models of ROV and the control system design before final hardware implementation. The control simulation using a proportional–integral–derivative controller is able to model the positions and velocities output of the ROV that is highly uncertain and nonlinear.

The focus of the research work presented in this article is the design of an industrial plant devoted to produce different types of hazelnuts-based products. The industrial plant design is initially based on a traditional approach (production process flow charts analysis and plant layout study) and it is then supported by a simulation model mainly used to investigate the system behavior in different operative scenarios. The industrial plant simulation model (called HAZIMUT, HAZelnuts Industrial plant design and management based on advanced simulation Models Utilization) recreates the entire hazelnuts production process (based on nine different production lines). The HAZIMUT simulation model is used to evaluate the effects — caused by multiple changes in the production lines capacities — on multiple performance measures based on machines levels utilization, work in process and production system productivity. The simulation results show how variations in production lines capacities generate an over-reaction of the system with major changes in some of the performance measures therefore stressing the importance to use the HAZIMUT simulator to tune the system correctly to improve the overall plant performances.