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An intelligent detection of the communication channel by the transceiver is what is known as cognitive radio (CR), a sort of radio communication. Avoid using channels that are already in use to reduce interference and swiftly direct traffic to open channels. In cognitive wireless networks, spectrum allocation has long been a research hotspot as one of the key problems to increase spectrum efficiency. This article examines the issue of spectrum sharing, in which one primary user (PU) might rent out portions of their spectrum to numerous secondary users (SU) in order to share it. The suggested Fuzzy Ant Colony Optimization based Multiple Scheduling Resource Selection Algorithm (FAMSRSA) chooses the best network while maximising data rate and minimising interference and cost. Here, Nash's balanced multi-antenna-based spectrum sharing model (NE-MASS) is used to optimize and share the spectrum. In this paper, a mathematical model optimizes multiple network selection goals to maximize the overall bandwidth, and the total cost and idle spectrum assignment are determined to a minimum in the context of a comprehensive consideration of differences in the spectral resource characteristics of primary networks that differ in network domain and diversify the user's secondary requirements and analyze the complexity of the problem in the user domain. This approach provides an efficient way to access the spectrum available to both primary and secondary users. As a result of the experiment, this algorithm shows the algorithm's efficiency better than the four conventional algorithms in terms of performance metrics like Maximized Minimum Bandwidth, Maximized Network Efficiency, Maximized Proportion Fairness, Relative error and Spectrum Utilization Efficiency. The performance of the NE spectrum allocation algorithm was evaluated by extensive simulation. The proposed spectrum allocation algorithm has shown superior channel allocation performance than other conventional literature algorithms. [ABSTRACT FROM AUTHOR]

The red palm weevil is one of the most dangerous pests affecting date palm trees in the Gulf region and the Middle East. This paper investigates the control of the red palm weevil (RPW) in date palms using several techniques, namely stem injection, sex pheromone traps, and the Sterile Insect Technique. A mathematical model describing the spread of the red palm weevil is presented, and the boundedness of the solution for the RPW system is studied. The conditions for local stability, forward bifurcation, and Hopf bifurcation are also obtained. Additionally, this research highlights the significance of optimal control in managing RPW and simulates the model using the Forward-Backward Sweep numerical method. It is observed that the parameters of the sterile insect technique, the pheromone trap, and the mechanical injection play important roles in controlling the RPW. Mechanical Injection may be more effective in controlling the palm weevil compared to the other two methods. [ABSTRACT FROM AUTHOR]

Tourist trip design is a fast-growing area of research. Tourist interest, budget, travel style, safety, and the existence of travelers with special needs (for example, wheelchair accessibility) are some of the elements to consider for obtaining personalized routes. Including these elements in a single mathematical model can considerably complicate the solution process. Also, route decision-making is affected by the context (health, social, political, economic, etc.) in which decisions are made. In this paper, the first aim is to propose a three-step methodology to obtain contextualized solutions to a tourist trip design problem (TTDP) with time-dependent recommendation factors. The methodology consists of (1) providing a basic TTDP model that avoids the complexity of including contextual information, (2) obtaining a set of solutions to the problem using a Modeling to Generate Alternatives (MGA) approach, and (3) using a recently developed a posteriori method to include the contextual information through fuzzy propositions. The second aim of the paper is to evaluate three algorithmic strategies for the MGA step. Lastly, considering a context for people with mobility impairments, an example is solved using real data. The results show the usefulness of the proposed methodology in solving the TTDP with contextual information. • A tourist trip design problem with time-dependent recommendations factors is solved. • A methodology to obtain contextualized solutions is developed. • An accessibility context is modeled with fuzzy propositions. • Solutions are obtained by using the modeling-to-generate-alternatives approach. • Best solutions are suitable for the accessibility context. [ABSTRACT FROM AUTHOR]

Mathematics plays a fundamental role in ecological research, yet its uses remain strikingly separate from advances in the environmental social sciences and humanities. In this paper, I work to address this impasse and outline the motivation and scope for an 'ecological mathematics', an approach to doing mathematics in environmental research which foregrounds relationship, embodiment and human difference. I begin by tracing the historical emergence of mathematics in ecology, noting how life processes have been conceptualised in a way which forces them to fit the ideals of mathematical models transplanted from the physical sciences. I then investigate the cultural factors shaping the evolution of mathematical thought, eliciting a malleability in how mathematical knowledge relates to the more-than-human world. This provides a place from which to rethink the role of abstraction in ecological thought, and develop mathematical methods grounded in ecological concepts. Drawing on ethnographic and perceptual accounts of space and time, I work with topological concepts from both mathematics and the social sciences to suggest a new correspondence between these subjects, elaborating a way of employing mathematical techniques which enliven, rather than deaden, the ecologies under study. The paper concludes with important philosophical clarifications to the approach of an ecological mathematics. [ABSTRACT FROM AUTHOR]

Emerging Industry 5.0 pushes advanced automation towards resilient solutions with enhanced human role. Resilience as an ability to sustain processes in the face of disruptions and adversarial attacks requires careful modelling and simulation. Cellular automata are efficient mathematical models used to simulate the behavior of complex systems, which change their state based on a set of predefined rules. In this paper, we suggest several updates to cellular automata (particularly Conway's "Game of Life") to address resilience. These include "Life and Creation", "War and Peace", and their hybrid "War and Creation" capable of addressing the important components of resilience, such as controllable creativity and adversarial interactions. Inherited in these updates and known advantages of cellular automata, such as simplicity, emergent behavior, parallelism, and adaptability, makes it a powerful simulation tool for a wide range of Industry 5.0 systems that involve humans, smart infrastructure, their complex and adversarial interactions, safety, and resilience. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet applications, massive demand for cloud services has brought challenges to data center networks (DCNs). Traditional wired DCNs adopt multilayer structure, leading to the congestion in high-layer switches. Furthermore, complex cabling in wired DCNs affects the flow of cold air, reduces the cooling efficiency, and increases energy consumption. To solve these problems, in this paper, we introduce an architecture for intra-DCN based on free space optic (FSO) wireless communication. The proposed scheme exploits spatial position of FSO transceivers at different heights to realize obstruction-free transmission for inter-rack requests. We formulate the transceiver arrangement problem into a mathematical model while satisfying the requirement for direct line-of-sight communication between any pair of transceiver nodes. Moreover, we evaluate the network performance of the proposed scheme via simulation in terms of flow completion time, server throughput, and power consumption. Compared with traditional ones, our scheme can considerably decrease power consumption while obtaining better network performance. [ABSTRACT FROM AUTHOR]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with anyGWin a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches. [ABSTRACT FROM AUTHOR]

The optimization of train formation plan is a large-scale combinatorial optimization problem, which is difficult to solve. This paper mainly studies the optimization algorithm of the single-block train formation plan. The corresponding mathematical model is established by consulting relevant literature, and a positive feedback search algorithm based on absolute conditions is proposed. First of all, the wagon flow that meets the absolute conditions directly runs through train flow without making other choices. For the wagon flow that does not meet the absolute conditions, select the target station to reach directly according to the probability. The probability of wagon flow selecting a station is calculated according to the pheromone of the wagon flow at the station. At the same time, a pheromone update strategy with positive feedback mechanism is proposed to make the search process converge. Finally, the feasibility of the algorithm and the necessity of introducing absolute conditions into the algorithm are verified by taking eight technical stations in the linear direction of the road network as examples. [ABSTRACT FROM AUTHOR]

Membrane-based purification of therapeutic agents has recently attracted global attention as a promising replacement for conventional techniques like distillation and pervaporation. Despite the conduction of different investigations, development of more research about the operational feasibility of using polymeric membranes to separate the detrimental impurities of molecular entities is of great importance. The focus of this paper is to develop a numerical strategy based on multiple machine learning methods to predict the concentration distribution of solute through a membrane-based separation process. Two inputs are being analyzed in this study, specifically r and z. Furthermore, the single target output is C , and the number of data points exceeds 8000. To analyze and model the data for this study, we used the Adaboost (Adaptive Boosting) model over three different base learners (K-Nearest Neighbors (KNN), Linear Regression (LR), and Gaussian Process Regression (GPR)). In the process of hyper-parameter optimization for models, the BA optimization algorithm applied on the adaptive boosted models. Finally, Boosted KNN, Boosted LR, and Boosted GPR have scores of 0.9853, 0.8751, and 0.9793 in terms of R2 metric. Based on the recent fact and other analyses, boosted KNN model is introduced as the most appropriate model of this research. The error rates for this model are 2.073 × 101 and 1.06 × 10−2 in terms of MAE and MAPE metrics. • Predicting concentration distribution of solute through a membrane-based process. • Machine learning model development using CFD data. • Optimization of hyper-parameters using BA algorithm. [ABSTRACT FROM AUTHOR]

This study focuses on an artery with multiple stenoses, emphasizing the electro-osmotic effects. The artery's walls are porous, and slip boundary effects are present. Blood ow problems are better modeled with a slip and porous border. It is examined extensively due to the wide range of applications in the medical field, especially in diagnosing drug delivery and handling cellular irregularities. In this paper, we have visualized the non-Newtonian behavior of blood by using viscoelastic fluids as Williamson's fluid model. A mathematical model for an incompressible fluid is created, and the mathematical issue is then transformed into its dimensionless form by applying limitations in the case of mild multiple stenoses. The partial differential equations for the velocity and temperature profiles can be found when the problem is put into a dimensionless form. Analytical solutions of the resulting system are calculated with the help of the Homotopy Perturbation Method (HPM). The visual representation of analytically obtained solutions is investigated for both symmetric and non-symmetric geometries of stenosis. For varied values of ow rate Q and electro-osmotic parameter m, the streamlines are examined in detail. [ABSTRACT FROM AUTHOR]

The recent outbreak of rabies virus has affected numerous individuals in the community, highlighting the importance of studying the disease mathematically in epidemiology. In this paper, we develop a mathematical model for the spread of rabies disease using the harmonic mean incidence rate and determine the reproduction number R 0 using the next generation matrix approach. The fractional dynamics of the model incorporate the interaction between infected individuals and environmental factors. We analyze the model using both Atangana-Baleanu-Caputo (ABC) and Caputo-Fabrizio (CF) techniques, drawing on both modern and classical approaches. For qualitative investigation using fractional operators, we utilize the Banach fixed-point theory and derive the Hyers-Ulam stability concept. We assign values to the model parameters and utilize the Newton interpolation technique to obtain a numerical scheme. Additionally, we perform sensitivity analysis on the model. In conclusion, our findings provide important insights for the study of epidemics. [ABSTRACT FROM AUTHOR]

In this paper, the stabilization and set point tracking of the utility boiler is achieved via the Proportional-Integral-Derivative (PID) controller, tuned using the versions of metaheuristic Particle Swarm Optimization (PSO) algorithms. The utility boiler model dynamics are based on the Bell and Astrom boiler turbine system. Initially, the non-linear mathematical model is obtained using the first principles data from the plant model. Further, Taylor series approximation is applied to linearize the non-linear equations around the equilibrium point to implement a stable controller for the plant. Finally, the PID controller gains for the plant model are determined using the PSO algorithms to achieve the desired response. For tuning the PID controller, three PSO algorithms namely standard PSO, Adaptive PSO (APSO) and Improved Adaptive PSO (IAPSO) are proposed. The performance of the proposed PSO based PID control algorithms are evaluated using error metrics such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). The simulation results show that the proposed APSO and IAPSO algorithms have improved performance when compared to regular PSO algorithms and are capable of optimizing the control parameters. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework that combines technology-based instruction and mathematical modeling in a STEAM-oriented approach to enhance the English language acquisition of Malaysian students. The proposed framework consists of a six-month English as a Foreign Language program that integrates technology and mathematical simulation in a STEAMoriented methodology. The exercises are designed to enhance linguistic competence, with a focus on improving listening, speaking, reading, and writing skills. The framework aims to foster a positive learning environment that encourages self-determination and promotes sociocultural interaction. The integration of technology-enabled instruction and mathematical modeling offers a viable strategy for enhancing the language competency of non-native English speakers. However, further research and empirical analysis are necessary to evaluate the impact of this framework on academic performance. Despite this limitation, the proposed framework offers a promising approach to address the challenges faced by less proficient Malaysian students in acquiring English language skills. In conclusion, this paper presents a conceptual framework that integrates technology-based instruction and mathematical modeling in a STEAM-oriented approach to enhance the English language acquisition of less proficient Malaysian students. The framework is grounded in sociocultural and self-determination theoretical perspectives and aims to create a positive learning environment that promotes linguistic competence. [ABSTRACT FROM AUTHOR]

In this digital, internet-based world, it is not new to face cyber attacks from time to time. A number of heavy viruses have been made by hackers, and they have successfully given big losses to our systems. In the family of these viruses, the Stuxnet virus is a well-known name. Stuxnet is a very dangerous virus that probably targets the control systems of our industry. The main source of this virus can be an infected USB drive or flash drive. In this research paper, we study a mathematical model to define the dynamical structure or the effects of the Stuxnet virus on our computer systems. To study the given dynamics, we use a modified version of the Caputo-type fractional derivative, which can be used as an old Caputo derivative by fixing some slight changes, which is an advantage of this study. We demonstrate that the given fractional Caputo-type dynamical model has a unique solution using fixed point theory. We derive the solution of the proposed non-linear non-classical model with the application of a recent version of the Predictor–Corrector scheme. We analyze various graphs at different values of the arrival rate of new computers, damage rate, virus transmission rate, and natural removal rate. In the graphical interpretations, we verify the values of fractional orders and simulate 2-D and 3-D graphics to understand the dynamics clearly. The major novelty of this study is that we formulate the optimal control problem and its important consequences both theoretically and mathematically, which can be further extended graphically. The main contribution of this research work is to provide some novel results on the Stuxnet virus dynamics and explore the uses of fractional derivatives in computer science. The given methodology is effective, fully novel, and very easy to understand. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model depicting the transmission dynamics of Chickenpox is developed by incorporating a new parameter denoting the rate of precautionary measures. The influence and the importance of following precautionary measures are showed by applying the real data collected at Phuket province, Thailand. The model analysis such as positivity and boundedness of the solutions are provided. The rate of precaution for the spread the of chickenpox was a factor that influenced the basic reproductive number, which was calculated using the next-generation matrix approach. The model's equilibrium points are identified, and the condition for the disease-free equilibrium's local and global asymptotic stability is established. The model also shows forward bifurcation. Numerical simulation is carried out to show the importance of considering the precautionary measures while controlling the disease spread and the influence of those introduced parameters are depicted graphically. Though our results, we concluded that the rate of precautionary measures plays an vital role at the same time it reduces the chance of getting infected by Chickenpox virus. • The Mathematical model is developed to understand the control of Chickenpox. • Diseases spread among people with and without complications were studied. • Stability of equilibrium points were examined by Routh and Castillo theorems. • While studying the influence of transmission rate, we ended up with forward bifurcation. • In our study of transmission rate, we discovered forward bifurcation. • We proved that, Multiple precautions = lower infection risk, reduced R 1. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Interuniversitaria de Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system. [ABSTRACT FROM AUTHOR]

Mitogen-activated protein kinase (MEK) inhibitors and oncolytic virotherapy are identified as promising cancer therapies that can enhance the efficacy of other cancer treatments. A few studies demonstrate that cancer cells proliferate when exposed to virotherapy with MEK inhibitors in an integer order model or without them in a fractional order model. None of them are intended to investigate tumor cell growth under the combined treatment strategy of chemo-virotherapy with a MEK inhibitor in a fractional order model. In this paper, a mathematical model based on fractional order ordinary differential equations (ODEs) is developed for the mutual interactions among tumor cells, as well as a therapeutic combination of chemotherapy, oncolytic viruses and the functional consequence of MEK inhibitor, to investigate how virotherapy could enhance chemotherapy under the action of MEK inhibitor. The numerical results show that virus burst size and MEK inhibitors have a noticeable impact on regulating the trend of tumor cell proliferation. While virotherapy responses to tumor cell proliferation are undoubtedly quicker than chemotherapeutic treatment responses, MEK intensity clearly affects the success of the treatment regimen. The results of this study can contribute to the development of a therapeutic strategy that combines MEK inhibitor functional monitoring with tumor cell growth control. [ABSTRACT FROM AUTHOR]

Traditional linear active disturbance rejection control (LADRC) may have difficulty to achieve a rapid precise disturbance rejection for a permanent magnet synchronous linear motor (PMSLM). By making use of model information, a model predictive and compensated LADRC (MPLADRC) method is proposed in this paper. In this method, a model compensated extended state observer (MESO) is designed to transform the controlled object into an established mathematical model through total disturbance compensation. Meanwhile, considering the delay problem of MESO, a phase advance module is designed to improve the estimation speed of MESO for system disturbance and state, thus the MESO can rapidly compensate various uncertainty disturbances to the controlled object in real time. The model predictive controller (MPC) is then designed based on the mathematical model, and its optimal control law is then obtained through a quadratic objective function to further suppress the disturbance unobserved by the designed MESO. The proposed method can thus realize a dual-degree-of-freedom disturbance rejection through the MESO and MPC. The simulation and experimental results validate the effectiveness of the proposed MPLADRC in rapid anti-disturbance and fast positioning for the motion control of the PMSLM. • An MPLADRC method for achieving real-time disturbance rejection is proposed. • A model compensation ESO is designed to accurately estimate the total disturbance. • A phase advance module is designed to solve the ESO delay problem. • A predictive controller is designed to further suppress the disturbance. [ABSTRACT FROM AUTHOR]

In this paper, harmonic resonance analysis of the grid comprising Wind Power Plants (WPPs) with Permanent Magnet Synchronous Generators (PMSGs) is presented. Usually, Norton equivalent circuit is used as the Wind Turbine (WT) model for harmonic resonance studies, in which the WTs are simply modeled as ideal current and voltage sources and the impacts of converters controls on the WTs output impedances are neglected. This paper deals with the harmonic resonance analysis of the WPP system by considering the impacts of converters controls and current and voltage measurement filters. In doing so, harmonic impedance models of the WTs are presented for simple and detailed cases. Then, the WT harmonic impedance is extracted for the mentioned cases. Next, the results of frequency scan and harmonic mode resonance analyses for different cases are given and compared. Then, the effects of the current and voltage measurement filters and WT converter control on harmonic resonance analysis of the study WPP system are presented. It is shown that increasing the bandwidth of the grid-side converter current control loop cannot shift the resonant frequencies, but it certainly enhances the system damping and, consequently, reduces the driving point impedances in the resonant frequencies. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

Copyright of Chemical Industry / Hemijska Industrija is the property of Association of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyzes the system describing the absorption solar collector as an application of nanoparticles for the storage of solar energy. The system involves two fractional partial differential equations (FPDEs) utilizing the Caputo fractional definition (CFD). Explicit solutions are determined for the temperature and velocity in terms of the Wright function (WrFn) via Laplace transform (LT). In addition, the solutions are expressed in terms of some well–known special functions at selected values of the fractional–order. Moreover, the behaviors of the temperature and velocity are investigated graphically using the thermo–physical data of the Cu/Al 2 O 3 –nanoparticles. Furthermore, some numerical results are conducted about the performance of the Cu and Al 2 O 3. The results reveal the higher efficiency/performance of the Cu–nanoparticles over the Al 2 O 3 and thus copper enjoys higher capability than alumina for the purpose of solar energy storage. In addition, it is found that 1.41% in the enhancement of heat transfer is achieved by adding 1% of the Cu–nanoparticles while the corresponding enhancement rate of the other three nanoparticles was less. Besides, the enhancement rate reaches 7.1% by increasing the volume fraction of the Cu–nanoparticles to 5%. Furthermore, the enhancement rates for the TiO 2 , Ag, and Al 2 O 3 are 5.41%, 6.47%, and 6.53%, respectively when using 5% of these nanoparticles. Finally, the advantages/effectiveness of the current approach over a previous work in the literature are discussed in detail. [ABSTRACT FROM AUTHOR]

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The manyobjective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of the behavior of a welded metal assembly using a specialized finite element analysis software. The aspect pursued is the determination of the nodal displacements that occur at a certain load, highlighting the areas of maximum stress. The paper also includes elements regarding the mathematical model of the studied phenomenon obtained with the help of variational methods and/or the method of weighted residuals. By modelling and simulating the dynamic behavior of the welded metal assembly, it is possible to determine the critical areas of welding deformations. The study carried out in this paper reveals the necessity of using numerical methods in the pre-design process of an engineering structure. [ABSTRACT FROM AUTHOR]

While disability is recognised by the United Nations Convention on the Rights of Persons with Disabilities (CRPD) as an evolving concept, the language of positive behaviour support has not kept pace with the current human rights-based approach. The widely-used terms 'challenging behaviour' and 'behaviours that challenge' imply that the behaviour is inherent in a person with disability. Words have power in shaping practice: when the behaviour of the person with disability is framed as the problem, this leads to a pathologisation or labelling that can provide a rationale to medicate and restrain as a way of 'managing the challenging behaviour'. Many behaviours seen as being challenging could be better understood as 'adaptive behaviours to maladaptive environments', or legitimate responses to difficult environments and situations. In this paper, we argue that the language and implementation of positive behaviour support should better take into account the CRPD and contemporary evidence on behaviour change interventions, which support a shift away from focusing on individuals' behaviours towards putting environments front and centre. We outline how the social-ecological model could be used as a framework to more explicitly address 'environments of concern' in developing tailored and system-wide responses to behaviour support needs. We argue there is an urgency for this paradigm shift to better reflect the views of people with disability and improve outcomes. [ABSTRACT FROM AUTHOR]

Ducts are conduits or passages used to supply, return, or exhaust air, needed for heating, ventilation, or air conditioning in multistory gigantic building structures. Rapid growth in population (especially in industrially concentrated regions) has led to the culture of constructing skyscrapers of tens of storeys, in which vertical ducts are the natural choice for removing polluted air. Due to the compactness of the building design, high voltage current-carrying wires are usually located very close to exhaust ducts carrying polluted air. Consequently, a natural motivation is to study how the magnetic field produced by such a wire affects the flow, driven by an external pressure gradient (for example, exhaust fan, vacuum pump, etc.) in a vertical duct. Therefore, the paper is devoted to understanding the interaction of the developed (thermally and hydrodynamically) flow in a vertical square duct with the magnetic field produced by a nearby placed current-carrying wire. The flow is assumed to be laminar and is driven by a constant external pressure gradient. For the thermal boundary condition, we assume a uniform heat flux across the unit axial length whereas a fixed temperature is also assumed over the peripheral surface of the duct. As a result of the mathematical modeling of the problem, we come across a system of coupled Poisson-like equations which are solved numerically by following a finite volume approach. We have noted that a sufficiently higher Rayleigh number may cause a reverse flow while reducing and flattening the thermal distribution in the middle of the duct. In addition the magnetic field further reduces the flow velocity, particularly near the duct wall where the current-carrying wire (causing the magnetic field) is positioned. [ABSTRACT FROM AUTHOR]

In order to alleviate urban congestion, improve vehicle mobility and logistics distribution efficiency, the urban logistics distribution system is regarded as a three-echelon logistics distribution system. In this paper, a mathematical model of the 3-echelon logistics distribution problem (3E-LDP) considering time window constraint is established based on the directed graph, and a double-tier intelligent algorithm solution scheme is proposed, which combines the distance entropy-based Affinity Propagation clustering (DEBAP) algorithm and the crossover and selection-based differential evolution algorithm (CSBDE). First of all, in order to reduce the scale of logistics distribution and improve the utilization rate of logistics distribution facilities, the DEBAP algorithm is proposed in the upper tier to divide the logistics distribution region and optimize the distribution of logistics facilities, and the resulting scheme is passed to the vehicle routing optimization algorithm in the lower tier. Secondly, the vehicle routes at all levels are optimized based on the CSBDE algorithm at the lower tier, and the optimized route scheme is fed back to the DEBAP algorithm at the upper tier, so as to coordinate multi-echelon logistics distribution. Then, a search strategy based on the reachable distribution region and a facility allocation optimization strategy based on the weight of routing length are proposed to improve the efficiency of the algorithm. Based on the above algorithms, the optimization of the three-echelon logistics distribution system is completed in coordination. Finally, the performance of the proposed method is evaluated on the standard benchmark instances of the problem. The experimental results show that the three-echelon logistics model can improve the efficiency of logistics distribution, and the method has the best comprehensive performance, which is better than the most advanced 3E-LDP solution method. It has great potential in practical projects. • A three-echelon logistics distribution model considering the time window constraint is established. • A double-tier intelligent algorithm is proposed for 3E-LDP. In the upper algorithm, an distance entropy-based Affinity Propagation clustering (DEBAP) algorithm is proposed. • In the lower tier algorithm, the differential evolution algorithm is improved, and the vehicle routings at all levels are optimized through positive output and feedback. • A search strategy based on the reachable distribution region and a facility allocation optimization strategy based on the weight of the routing length are proposed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Theory & Practice in Education (JTPE) / Eğitimde Kuram ve Uygulama is the property of Journal of Theory & Practice in Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In mobile wireless networks, the Root Sequence Index (RSI) is used to allocate uplink channels between the user equipment and the base station. The assignment of RSIs close-in-range to neighbor radios may cause collisions leading to failures on service establishment and performance degradation. In this paper, we model the RSI allocation as a generalization of the classical Graph Coloring Problem, indicating that there must be a minimum distance for the assigned colors of two neighbors. For the RSI allocation, a maximal distance is also needed. We develop methods for allocating the RSI, while minimizing the risk of collisions, for two different operation modes found on carrier-grade networks. Both exact and heuristic methods are explored and compared. We test the proposed approaches on instances obtained from real-life context. • The root sequence index allocation problem is proposed and detailed. • 139 real-life based instances are introduced. • Exact and evolutionary approaches are developed, for two differing objectives. • Computational experiments indicated better performance in the evolutionary approaches in comparison with the exact ones. [ABSTRACT FROM AUTHOR]

High-end equipment manufacture has features of intensive technology, uncertain process, and large scale. Hence, the scheduling of the High-end equipment manufacture is challenging. Large ships are a type of High-end equipment that is tied to national maritime security. In this paper, we study a stochastic hierarchical scheduling problem with serial-batch processing machines and the deterioration effect in ship manufacture. In the studied problem, the job processing time is uncertain and follows a normal probability distribution. Under the deterioration effect, the actual processing time of jobs is formulated as a function of the start time. The first objective is to maximize the robustness of the manufacturing system. The second objective is to minimize the total inventory cost subjected to the optimization of the first objective. The robustness of the manufacturing system is measured by the worst level of completion probability. The total inventory cost is measured by the difference between the job delivery time and the job completion time. The mathematical model for the studied problem is formulated. Some structural properties are derived to solve the problem. Based on the properties, the optimal sequence for the first objective can be obtained and a heuristic algorithm is designed to optimize the second objective. The considered problem is NP-hard involving several non-linearities. Hence, a variable neighborhood search (VNS) algorithm is developed to solve the problem in a reasonable time, which is integrated with three local search strategies and a heuristic algorithm. Various problem instances are generated and extensive computational experiments verify that the proposed VNS algorithm outperforms other compared algorithms and has strong robustness. • We model a bicriterion scheduling problem with the deterioration effect and stochastic processing time. • A schedule rule is presented to identify the optimal sequences under the first objective (robustness maximizing). • A heuristic is developed based on structural properties to reduce the total inventory cost. • A variable neighborhood search algorithm is constructed by combining heuristics and three local search algorithms. [ABSTRACT FROM AUTHOR]

In today's competitive world, assembly lines are one of the most important industrial manufacturing systems, which can accommodate the massive increase in customer demands and product range, for the production industry. One possible way to increase the capacity and efficiency of assembly lines is to design a parallel assembly line system, consisting of two or more assembly lines placed next to each other. In the literature, the assembly lines are usually balanced according to tasks with fixed processing times without considering worker–task compatibility. In real-life applications, however, since the processing time of each task may vary according to the capability of each worker, the assignment of tasks to stations in the assembly line depends on the skills of the worker assigned to the related station. Considering the worker and task assignments together for each assembly line will provide a more realistic perspective. This paper, to the best of our knowledge, is the first study that considers the minimization of joint cycle time for the worker assignment and assembly line balancing problem in parallel assembly lines (PALWABP-II). Accordingly, a binary linear mathematical programming (BLP) model is developed and an artificial bee colony (ABC)-based solution approach is proposed for medium and large-sized problems. A new set of test problems is presented to the literature for a computational experiment. In addition, a summary of the literature is presented. An example problem is solved using the BLP model. In order to evaluate the effectiveness of the proposed model and approach, they are compared with the classical particle swarm optimization (PSO) algorithm and ten different priority rules. The computational results on test problems prove the effectiveness of the proposed ABC algorithm for the PALWABP-II. • This study considers the joint cycle time for the worker assignment and assembly line balancing problem in parallel assembly lines. • A new binary linear programming model is developed for the problem. • A new artificial bee colony-based solution approach is proposed for the medium and large-sized problems. • A new set of test problems is generated for the computational experiments. • A summary of literature is presented for both worker assignment and parallel assembly lines. [ABSTRACT FROM AUTHOR]

As an important part of cloud manufacturing, service composition and optimal selection (SCOS) has attracted many scholars' attentions. Although there are a large number of researches devoted to this field, plenty of challenges still remain, such as the difficulties of coping with real-life uncertainties and improving efficiency in making decisions. To this end, a bi-objective service composition and optimal selection (BoSCOS) problem which takes both QoS and robustness into account is firstly studied in this paper, its mathematical model is established after the two criteria are constructed. Then, a strengthened multi-objective gray wolf optimizer (SMOGWO) is developed to solve the above model with high efficiency, in which three improvement strategies are introduced to the original MOGWO: (1) the opposition based learning (OBL) strategy is adopted to improve the quality of initial population; (2) a leader update strategy is designed to explore better search leaders and balance the exploitation and exploration abilities; (3) an improved archiving strategy is developed to retain all possible dominant individuals, so as to improve the population diversity based on the Pareto dominance. Finally, the effectiveness of the proposed algorithm is verified on 8 BoSCOS problems with different scales and 17 benchmark functions, results show that the improvement strategies are effective and SMOGWO is superior to its competitors in terms of convergence and population diversity. • A BoSCOS problem considering both QoS and robustness is firstly proposed. • The robustness of a CMS is formulated in terms of response time caused by exceptions. • A leader update strategy is designed to explore better search leaders for SMOGWO. • An improved archive mechanism is developed to improve the population diversity. • Experimental results show that SMOGWO is obvious superior to common-used algorithms. [ABSTRACT FROM AUTHOR]

This manuscript discusses the positional systems used in intelligent robots. One of the characteristic problems that arise in such systems is associated with friction, including sticking during the process. Friction greatly affects the positioning accuracy of the actuators of controlled robots, especially if a pneumatic actuator is used as a motor. The work is devoted to the study and solution of this problem. Since it is impossible to completely eliminate the effect of friction, ways to minimize this effect are being investigated using a targeted choice of system parameters. For this purpose, the method of multicriteria optimization of a detailed mathematical model is applied on the example of a pneumatic positional system, as the most sensitive to friction. The paper considers the first stage of optimization associated with visualization and preliminary analysis of the process in order to prepare it for performing a system procedure. The dynamics equations of an intelligent positional system are written out, both in full-size and dimensionless one. The most convenient mathematical model of friction is presented terms of its use in the process of optimizing the entire control system. A procedure for visualizing the optimization process in the form of a graphical interface has been developed. The first stage of the intelligent drive system optimization procedure has been carried out. The paper presents at which parameters of the control system the "stick-slip" effect has less influence on the positioning process. [ABSTRACT FROM AUTHOR]

The mathematical modeling and the control problem of the paraglider recovery system (PRS) are investigated in this paper. A 9-degree-of-freedom dynamic model describing the paraglider recovery control system is modeled including the inside relative rotation, the apparent mass, and the payload's time-varying inertia. On the basis of the attitude tracking error system, a novel practical fixed-time attitude tracking control approach is then proposed. The attitude tracking error is governed to converge into the small regions of the origin with a fixed-time convergence rate. Moreover, this rate is independent of any initial states. Simulation results are finally presented to illustrate the PRS performance obtained from the proposed control scheme. • A new high-fidelity model of the paraglider recovery system is established with the payload's time-varying inertia considering. • A user-friendly model that facilitates the control design is derived from the high-DOF model. • A novel practical fixed-time attitude tracking controller is developed. • Precise attitude tracking performance and fast convergence rate are achieved by the proposed scheme. [ABSTRACT FROM AUTHOR]

A new improved three-dimensional continuous chaotic system (ImproBsys) is designed in this paper. It can achieve from an ordinary chaotic state to a hyperchaotic state, that is, chaotic behavior tends to become more complex. Furthermore, by using ImproBsys, this paper proposes a double image encryption algorithm based on compressive sensing and public key elliptic curve. First, the two plain images of the same size are excuted by discrete wavelet transformation (DWT), and then the DWT coefficients are thresholded. Second, the quantization matrix is compressed by compressive sensing, and the size is reduced to half of the original one, and then the two compressed matrices are spliced together to form a new matrix. Finally, the new matrix is encrypted by elliptic curve cipher to get the cipher image. Our contributions are: (1) A new ImproBsys is designed with better chaotic behavior, which has two positive Lyapunov exponents to show hyperchaos phenomenon. (2) Compressive sensing technique is employed to reduce the amount of data transmission for two single images, and then we use ImproBsys to control the measurement matrix. (3) The initial values of the ImproBsys depend on the information entropy of the plain image by a new constructed mathematical model. [ABSTRACT FROM AUTHOR]

With the development of communications and transportation worldwide, the challenge of controlling epidemiological diseases becomes higher. The COVID19 has put all nations in a lethal confront with a severe disease that needed serious and painful actions. The sooner the actions, the less destructive the impact. In this paper, we incorporate what we believe is crucial but applicable to control the spread of COVID19 in the populations, that is, quarantine. We keep the model as simple as SI Kermack-McKendrick model with an additional compartment of quarantined patients. We established the system's basic properties and studied the stability of the disease-free equilibrium and its relation to the basic reproduction number R 0 in which we calculated its formula. The focus of our study is to measure the effect of quarantine rate on controlling the spread of COVID19. We use the data collected from the Ministry of Health in Saudi Arabia. We studied three different values of the quarantine rate where newly infectious patients are detected and isolated within 14, 7, and 5 days. The simulations show a significant effect of the quarantine where COVID19 can be fully controlled if the newly infected patient enters the quarantine within five days. These results were proposed to the Public Health Authority in Saudi Arabia and approved by the Ministry of Health in which they applied promptly. [ABSTRACT FROM AUTHOR]

This paper presents a novel directional observer-based fault detection and isolation scheme for second-order networked control systems (NCS). The directional unknown input observer (UIO) tool is exploited to study the problem of distributed fault detection and isolation (FDI). Two design schemes with global and partial/local network models are proposed to solve the distributed FDI problem. Thresholds are computed for the application of the proposed schemes in a noisy environment. In addition, the salient features of the proposed schemes are that both fault detection and fault isolation are achieved in a single step using a single observer. The schemes are applied to power system models to validate their results. A detailed comparison with existing FDI schemes is also provided, which clearly shows the effectiveness of the proposed scheme in terms of computational requirements. • The direction unknown input observer is used for distributed fault detection and isolation. • Distributed FDI schemes with and without complete mathematical model of the system are presented. • The developed schemes are more efficient in terms of computational burden. [ABSTRACT FROM AUTHOR]

Since the observed angle of a phase-locked loop (PLL) is relatively accurate even under distorted grid conditions, the mathematical model of the voltage errors caused by the switching modulation and the dead-time effect are derived as a function of the grid voltage angle in this paper. Based on the model analysis, an adaptive compensation algorithm is proposed to suppress the grid-side current harmonics in three-phase converters. The proposed algorithm fits the unmeasurable voltage errors by a truncated Fourier series expansion, and then takes it as an equivalent disturbance in the current control loop to achieve harmonic compensation. By the feed-forward compensation, the design and tuning of the controller parameters are simplified and separated from the dynamic performance. In addition, the controller can adapt to the grid frequency variation by updating the grid voltage angle with the PLL block. To reduce the computational burden, a simplified version of the proposed method is also presented. Simulation and experiment results show that the proposed methods can suppress the current harmonics and achieve better performance in terms of total harmonic distortion, strong robustness and insensitivity to the grid frequency variations, compared with the traditional PI control or repetitive control strategy. [Display omitted] • A PI controller is designed to ensure satisfactory dynamic performance. • An adaptive method is employed to compensate the voltage errors caused by the switching modulation and the deadtime effect so as to suppress the grid current harmonics. • A simplified adaptive strategy is also presented to reduce the computational burden and the complexity to half of the original method. • The adaptive algorithm is less model-dependent and built based on the grid voltage angle with a PLL to achieve frequency adaption. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to address coherent interference (pilot contamination) in Massive MIMO by incorporating control over propagation environment through Intelligent Reflecting Surfaces. In earlier research it was established that each mobile device has unique spatial signature which can be used as identifier even if they are sharing same pilot sequence. However in certain circumstances when their signals are received at BS with same Angle of Arrival their spatial structure become similar thus their individual identity is lost. This situation adds error in acquisition of channel state information, the accuracy of which is the key to benefit from Massive MIMO. This paper present a method to resolve the mentioned accepted gap using mathematical basis and simulation methods. A mathematical model of Massive MIMO is presented which is widely accepted by the subject modeling community. The model is then used to generate the basis for the presented algorithm through further mathematical studies and simulations. The basic algorithm used for the resolution of pilot contamination problem is finally presented in this paper. The algorithm was simulated using MATLAB and simulation results are tabulated to identify the results of pilot de-contamination after incorporating signal propagation control. [ABSTRACT FROM AUTHOR]

This paper discussed the relationship between angle and the value PvGRF for the execution of parachute landing. The value of PvGRF has been determined by using mathematical modelling principle of conservation of energy. The mathematical model describes the correlation between peak vertical ground reaction force and drop height. The peak vertical ground reaction force was used to determine the amount of force exerted on the parachutist during landing. A total of 21 parachutists from the Malaysian Army participated in this study comprising of three professional and eighteen amateur parachutists. Two cameras were utilised to capture the movements and thus generate kinematic information throughout the landing phase. The results showed that the estimated maximum peak vertical value of ground reaction force during the foot strike phase for professional and amateur parachutists were 3129 N and 4380 N respectively. Furthermore, the data showed that the professionals bent their hip and knees before their foot strike the ground. From the principle of biomechanics, this action increases the time and drop height during landing and thus reduce the peak vertical ground reaction force. Therefore, the hip and knee movement of the professional parachutist acts as a guideline for amateurs to reduce the impact force during landing. [ABSTRACT FROM AUTHOR]

Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper contains the study of Multi skill training to the employees, which includes equalizing the work pressure among the employees and managing the shortage of employees in the manufacturing process. A mathematical model was formed with an objective of minimizing the total expected cost of the employees in the manufacturing process. AHybrid Genetic algorithm was used to stimulate the solutions. The results showed that the multi skill training program yields a better result compared to non-trained employees. [ABSTRACT FROM AUTHOR]

The paper presents the optimization of the geraniol transformation process based on the RSM method (response surface methodology). RSM is a fusion of statistical and mathematical modeling methods and explores the relationships between several explanatory variables and one or more response variables. The interaction among process variables can be determined by statistical techniques. It can be utilized in multi-criteria optimization. The influence of the most important process parameters, such as temperature, catalyst concentration and reaction time, is shown. The response functions characterizing the process were: conversion of geraniol, selectivity of thunbergol and selectivity of 6,11-dimethyl-2,6,10-dodecatrien-1-ol. For individual process parameters, their influence on all initial parameters in the form of second-degree polynomials was determined and such as process conditions were determined to achieve their maximum. [ABSTRACT FROM AUTHOR]

With the continuous development of economic globalization and the increasing strengthening of human exchanges, the Water–Energy–Food (WEF) nexus evaluation has become a new research area. The purpose of this paper is to develop an inverse approach of graph model for conflict resolution (GMCR) to the conflict problems of WEF nexus evaluation in real life. Specifically, due to lack of information, some decision makers' (DMs') preferences over states may be incomplete fuzzy preference relations. Therefore, an algorithm is devised to amend the incomplete fuzzy preference relation to the complete fuzzy preference relation. Subsequently, a complete ordinal score vector is proposed to describe the preference ranking over different states based on the complete fuzzy preference relation. Moreover, in the framework of the inverse approach of GMCR, some mathematical models with the least constraint conditions are proposed to obtain all the required preference relations for opponent DM, which are required to make a given state be stable under four basic stability definitions. Finally, WEF nexus evaluation in Shandong province is illustrated to demonstrate the usefulness of the inverse approach of GMCR with the incomplete fuzzy preference relations. • An inverse approach of graph model is developed based on incomplete fuzzy preference relation. • The proposed method is helpful to model and analyze the WEF nexus evaluation conflict. • The paper integrates the IFPR into the inverse approach for the graph model. • The paper provides a new research perspective for the inverse approach for the graph model. • The proposed method promotes the development and expansion of the graph model. [ABSTRACT FROM AUTHOR]

In this paper, a fractional-order SIRD mathematical model is presented with Caputo derivative for the transmission of COVID-19 between humans. We calculate the steady-states of the system and discuss their stability. We also discuss the existence and uniqueness of a non-negative solution for the system under study. Additionally, we obtain an approximate response by implementing the fractional Euler method. Next, we investigate the first and the second waves of the disease in Iran and Japan; then we give a prediction concerning the second wave of the disease. We display the numerical simulations for different derivative orders in order to evaluate the efficacy of the fractional concept on the system behaviors. We also calculate the optimal control of the system and display its numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we have considered a mathematical model that deals with the effectiveness of the measures that may be helpful for reducing the spread of the COVID-19 virus in the society. Here we have illustrated the importance of lock down in controlling and maintaining the spread of the COVID-19 virus. The impact of the virus on the susceptible population has been considered in the model. Also, we have taken into account the susceptible population, which by taking preventive measures viz., by having strong immunity, maintaining social distancing, wearing PPE kits and masks etc., is able to reduce the possibility of getting infected from the virus. Local as well as global stability of the equilibrium points of the model have been studied using Lyapunov function and the geometrical approach techniques. Basic reproduction number has also been obtained by using the next generation matrix. To show the effectiveness of the model, different cases obtained by varying the parameters involved in the model have been considered. A comparison between the actual number of infected cases in India and that obtained by the proposed model, showing the effectiveness of the proposed model, has also been carried out. [ABSTRACT FROM AUTHOR]

In the practical applications, the mass of a quadrotor unmanned aerial vehicle (UAV) would be time-varying. This time-varying mass will deteriorate the control performance of UAV. To solve this challenge, the mathematical modeling problem of a quadrotor UAV with time-varying mass is first investigated in this paper. The nonlinear dynamics describing the six-degrees-of-freedom full motion is established. Based on the established model, taking attitude tracking control problem into consideration, a robust control scheme is then designed via the sliding mode control theory. Applying the developed proposed approach, the desired trajectory is followed with the attitude tracking error asymptotically stabilized. The proposed controller has the capability of rejecting the external disturbance and the uncertainties induced by the time-varying mass. The feasibility of the established model and the effectiveness of the presented control approach are validated through a simulation study. [ABSTRACT FROM AUTHOR]