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Previous studies have demonstrated that students who are engaged in learning tasks and make errors before receiving instruction on how to complete them, achieve better learning outcomes than students who first receive instruction and then complete the learning activities with the aim of avoiding errors. Although simulation literature often refers to errors as learning opportunities, to date, there is limited understanding of how pedagogical approaches that promote learning from errors can guide the design of simulation-based learning in healthcare education. To (a) present the Learning from Errors conceptual model; and (b) provide an example of how educators can use this model. The Learning from Errors model is drawn from critical elements of two pedagogical approaches, productive failure and error management training and pedagogical features of high-quality healthcare simulations. We describe the Learning from Errors model, which emphasises the need for adopting pedagogical methods that explicitly use errors as learning opportunities and ultimately inform simulation design. We then illustrate the application of this model to a simulation example. The model includes the following elements: i) normalisation of errors, ii) challenging simulation scenarios, iii) self-directed learning, iv) collaborative teamwork and v) comparison with best practice. This discussion paper presents the Learning from Errors conceptual model, an evidence-based approach that can assist educators in the design of simulations that embrace errors as a catalyst for learning. [ABSTRACT FROM AUTHOR]

For the stability of composite fibre spinning, the difference in and distribution of the polymer melt velocity during the spinning are among the factors of importance. Based on the basic equation for the control of composite spinning dynamics, boundary conditions are identified and reported in this paper. A mathematical model for the symmetric and asymmetric distribution of the melt flow velocity in the microhole of the spinneret of the composite spinning assembly was developed. The accuracy of the mathematical model was also ascertained. It gives a theoretical basis for the designing of a composite spinning assembly. [ABSTRACT FROM AUTHOR]

This paper introduces a novel pseudo-random sequence generator, applicable across all uses of pseudo noise (PN)-sequence. The proposed generator, coined as the circular pseudo-random signal generator, embodies a unique fusion of graphical representation and mathematical modeling. The cornerstone of this method is its capability to offer variable configurations in pseudo-random sequence generation, enabling the adaptive operation of the pseudo-random sequence between the transmitter and the receiver. Uniquely, the circular pseudo-random Sequence Generator can generate pseudo-random sequences of varying lengths, with practical implementation feasible through multiple methodologies, including microcontrollers or field-programmable gate array (FPGA) technology. Consequently, the paper endeavors to elucidate the mathematical model of generation, supplemented with illustrative examples, and demonstrate the real-world implementation using FPGA technology. With broad applicability, this sequence generator is well-suited to all applications requiring such a generator, notably in security applications and pilot generations. [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 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]

The significance of mental health inequities globally is illustrated by higher rates of anxiety and depression amongst racial and ethnic minority populations as well as individuals of lower socioeconomic status. The COVID-19 pandemic has further exacerbated these pre-existing mental health inequities. With rising mental health concerns, arts engagement offers an accessible, equitable opportunity to combat mental health inequities and impact upstream determinants of health. As the field of public health continues to shift its focus toward social ecological strategies, the social ecological model of health offers an approach that prioritizes social and structural determinants of health. To capture the impacts of arts engagement, this paper creates an applied social ecological model of health while aiming to advocate that engaging in the arts is a protective and rehabilitative behavior for mental health. [ABSTRACT FROM AUTHOR]

Community health workers (CHWs) and promotores de salud are frontline health workers who typically come from the communities they serve. Despite providing crucial services, they are not institutionalized (or integrated) within much of the U.S. health care system. Many work, either officially or unofficially, as medical interpreters--restricting their full impact as CHWs/promotores. In this paper, we detail the misemployment and its effects among a subsample of CHWs/promotores in two geographically distinct, exploratory projects. We encourage that collaborative research with CHWs/promotores continue and that fidelity to the CHW model be ensured to realize their true potential. [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]

Robust and transparent formal benefit-risk (BR) analyses for medicinal products represent a means to better understand the appropriate use of medicinal products, and to maximize their value to prescribers and patients. Despite regulatory and social imperatives to conduct structured BR (sBR) assessments, and the availability of a plethora of methodological tools, there exists large variability in the uptake and execution of sBR assessments among pharmaceutical companies. As such, in this paper we present an sBR assessment framework developed and implemented within a large global pharmaceutical company that aims to guide the systematic assessment of BR across the continuum of drug development activities, from first-time-in-human studies through to regulatory submission. We define and emphasize the concepts of Key Clinical Benefits and Key Safety Risks as the foundation for BR analysis. Furthermore, we define and foundationally employ the concepts of sBR and a Core Company BR position as the key elements for our BR framework. We outline 3 simple stages for how to perform the fundamentals of an sBR analysis, along with an emphasis on the weighting of Key Clinical Benefits and Key Safety Risks, and a focus on any surrounding uncertainties. Additionally, we clarify existing definitions to differentiate descriptive, semi-quantitative, and fully quantitative BR methodologies. By presenting our framework, we wish to stimulate productive conversation between industry peers and health authorities regarding best practice in the BR field. This paper may also help facilitate the pragmatic implementation of sBR methodologies for organizations without an established framework for such assessments. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective mathematical model to optimize and harmonize a supply chain in order to reduce costs, improve quality, and gain a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to enhance both quality and customer satisfaction and reduce the production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was first validated. The parameters of the proposed algorithm were then adjusted to solve the model using Multi-Objective Simulated Annealing (MOSA) algorithm. To validate the performance of the designed algorithm, some examples were solved based on General Algebraic Modeling System (GAMS). The MOSA algorithm achieved average errors of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in the average less than one minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS failed to reach an optimal solution for large problems in a reasonable computational time. The average error of the designed algorithm was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper. [ABSTRACT FROM AUTHOR]

This paper discusses a particular situation in project management in which an analyst attempts to prioritize several independent activities to handle all of them one by one in such a way that there would be no precedence relationships over the activities. The novelty of this research is that the structure of prioritized activities is linear in arrangement which can be considered as a combinatorial optimization problem. The paper formulates a mathematical model and applies it to two real cases in the oil and gas industry. In addition, a row generation procedure is developed to solve largescale problems and the computational results for the problem instances of size up to 300 activities are reported. The results demonstrate the applicability and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

An integrated decision in supply chain is a significant principle for proper competition in today's market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to make a coordination between procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi's experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

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.)

The article analyses the evolution of mathematical models used in life cycle estimation. A comparison between the different mathematical models and their areas of applicability is proposed. This analysis is generally done in terms of costs. The importance of the article lies in the proposal of a new mathematical model for estimating life cycle of power substations, compatible with current and future requirements. The study findings reveal that an optimization model can be established using the life cycle assessment method to obtain a longer lifespan, but also to minimize costs (maintenance, repairs, replacements, etc.). In order to establish a mathematical model of the life cycle of electrical substations, it is necessary to study several important design parameters and decision factors for each stage of the life cycle. Even if the paper does not provide empirical results, it must prove that it is necessary to develop a multi-algorithm optimization framework to find an adequate solution. The life cycle must be modelled according to: equipment availability technological evolution, maintenance activities, risk management, environmental protection, human error, etc. The study helps further research to build, implement and validate a robust model with development possibilities depending on the needs of the researchers. [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]

Industrial clusters are one of the most current development models. Aggregation of firms in a geographical area has many advantages, such as cost reduction, better supply, and knowledge emission with linkage together. The linkage result will be created the networks. The industrial clusters without co-operation networking will not be developed. That must be noticed to severe changes of business environment parameters. Therefore, this paper develops an uncertain mathematical model under sustainable and dynamic conditions. The model contains four objectives, namely profit, transportation cost, employment, and environment appraisal of the cluster. The outcome of this research is to find the best/optimal solution for firms’ arrangements with/within networks that maximize the profit, employment, and environment score and so minimize the transportation cost. The assignment patterns show horizontal and vertical cooperation with/within networks. The efficiency of model clustering in sub-clusters is followed by the neighbor clustering efficiency and the one’s clustering efficiency methods. [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]

In the present paper, an attempt is made to construct a deterministic mathematical simulation for population systems, by which their temporal (equation of motion) and spatiotemporal (equation of distribution) behaviour can be deduced, as solutions of the constitutional differential equations of the system. The generic formulation of the constitutional equations gives the simulation the possibility to expand to several populations, but also to parameters of different nature (say economic), by applying proper transformations according to the inner properties of each parameter. The introduction of the topographical features of such a system can be reduced to a boundary conditions problem, applied to the constitutional differential equations. Two initial applications are analyzed herein, namely a one-dimensional inertial population system, and a one-dimensional dynamic population system, where the external force corresponds to a space of constant curvature. The theoretically predicted behaviors of the population distribution of these systems are compared qualitatively to actual field data, collected from cities around the World. [ABSTRACT FROM AUTHOR]

The electric vehicle battery state prediction in real time is an important issue to avoid the risks of burning the battery due to over-charging or dead batteries that are caused by aging. Based on the past works, it is found that the State of Charge (SOC) can be predicted, while predicting the State of Health (SOH) is a difficult challenge. Usually, the SOH is predicted after the end of the driving or the charging cycle under constant conditions; this method is practically impossible because the battery can reach the end of the battery life before achieving the prediction process. In this paper, a SOH prediction method is proposed based on SOC prediction because there is a relation between the SOC and the SOH as indicated by deriving a mathematical model. The prediction process of battery age is achieved during the beginning of the battery charging process under constant conditions of charging, in which a SOC estimation has been implemented by the nonlinear auto-regressive with exogenous input neural network (NARX) with two initial values of SOC, the default value (0%) and practical value (10%) and two charging current rates (0.5C and 1C). The proposed method has been simulated by MATLAB, which several scenarios have been achieved to validate the proposed method. The root-mean-square error (RMSE) values are very promising for both predicting SOC and SOH that are 0.5% and 0.018%, respectively. [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]

The classical manufacturing systems assume that all produced items are of perfect quality. They also need to consider the rework process in manufacturing operations. Moreover, most of the previous literature considers single-stage production-inventory systems and does not consider multi-stage options. However, in real-world production-inventory systems, defective items are inevitable, and a fraction of the produced items may be defective. In addition, to avoid extra costs and consider environmental issues, organizations tend to rework activities. We propose single and multi-stage production-inventory systems in manufacturing operations where the process is defective, rework is possible, and a percentage of items are scrapped. A main assumption in the current paper is that the defective rate is assumed to be an uncertain parameter. The grey systems theory, as a mathematical tool to address uncertain information in real-world situations, is utilized to model the random defective rate via a grey nonlinear programming problem. The proposed issues are investigated via numerical examples to assess the impact of grey parameters on optimal solutions. [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]

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]

Oil waste is one of the most important pollutants in the oil and gas industry. Since the oil owing in the wells contains significant amount of saltwater, the effluent amount rises upon increasing the oil reservoir extraction. Separating the saltwater from the extracted oil before starting the refinery process plays an essential role in reducing the oil costs and benefiting from the transfer capacity as well. This paper presents a new Chapman-Kolmogorov Equation-Based (CKEB) method to evaluate the availability of a desalination system with three-state equipment and weighted-k-out-of-n configuration. In this system, the equipment is repairable, and each repair facility can repair all equipment types of different sub-systems (pump stations). All failures and repairs were considered to have a constant rate (with exponential distribution) and use the Chapman-Kolmogorov equations to drive the system availability. Then, the presented method was validated using a simulation technique. Finally, the elapsed times of solving both techniques were calculated and compared. The results confirmed the superiority of the CKEB technique in terms of computational time. Compared with the simulation technique, the computational time ratio for the CKEB method was in the range of 0:0002%0:0058% for the small-size problems, 0:05%0:94% for the medium-size problems, and 1.31%-5.39% for the large-size problems. [ABSTRACT FROM AUTHOR]

Many information technologies for numerical analysis of electrical circuit parameters have been developed. The peculiarity of these methods is their applicability not only to DC but also AC circuits, not only to linear but also nonlinear circuits. This paper introduces the special mathematical model for rectifier circuits using semiconductor diodes. In previous studies, the rectifier circuit has been modelled by differential inclusions. However, the inclusions cannot be solved by the applied programs of information technology. In this paper, the new model writing by differential equation with a special right hand side is studied to replace the classical model. The main result of this is presented in a theorem that gives the correctness of the new model. The usefulness of this study is illustrated via a concrete example. [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]

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]

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]

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]

Copyright of Paradigma is the property of Universidad Pedagogica Experimental Libertador 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 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]

Assessing uncertainty in decision-making is a major challenge for DecisionMakers (DMs), and the g-Rung Orthopair Fuzzy Set (q-ROFS) as the direct extension of Intuitionistic Fuzzy Set (IFS) and Pythagorean Fuzzy Set (PFS) play a crucial role in this aspect. The Complex g-Run Orthopair Fuzzy Set (Cq-ROFS) is a strong tool to deal with imprecision, vagueness, and fuzziness by expanding the scope of Membership Degree (MD) and Non-Membership Degree (NMD) of q-ROFS from real to complex unit disc. In this paper, we develop some new Cq-ROF Hamacher Aggregation Operators (AOs), i.e., the Cq-ROF Hamacher Weighted Averaging (Cq-ROFHWA) operator, the Cq- ROFH Weighted Geometric (Cq-ROFHWG) operator, the Cq-ROFH Ordered Weighted Averaging (Cq-ROFHOWA) operator, and the Cq-ROFH Ordered Weighted Geometric (Cq-ROFHOWG) operator. Subsequently, we establish a novel Cq-ROF graph framework based on the Hamacher operator called Cq-ROFH Graphs (Cq-ROFHGs) and evaluate its energy and Randic energy. In particular, we compute the energy of a splitting Cq-ROFHG and shadow Cq-ROFHG. Further, we describe the notions of Cq-ROFH digraphs (Cq- ROFHDGs). Moreover, an algorithm is given to solve Multiple Attribute Group DecisionMaking (MAGDM) problems and the main steps are discussed clearly. Finally, a numerical instance related to the Facade Clothing Systems (FCS) selection is presented to show the effectiveness of the developed concepts in decision-making circumstances. In order to verify the effectiveness of our proposed scheme, a comparative analysis with previous approaches is provided. [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.)

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]

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]

In this paper, a novel bi-objective mathematical model is proposed to design a four-dimensional (i.e., part, machine, operator, and tool) Cellular Manufacturing System (CMS) in a dynamic environment. The main objectives of this model are to: (1) Minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators' educational costs and (2) Maximize the skill level of operators. The developed model is strictly NP-hard and exact algorithms cannot find globally optimal solutions in a reasonably computational amount of time. Thus, a Multi-Objective Vibration Damping Optimization (MOVDO) algorithm with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in a reasonably computational amount of time. Since there is no benchmark available in the literature, three other meta-heuristic algorithms (i.e., Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Invasive Weeds Optimization (MOIWO)) with a similar solution structure are developed to validate the performance of the proposed MOVDO algorithm for solving various instances of the developed model. The result of comparing their performances based on statistical tests and different measuring metrics reveals that the proposed MOVDO algorithm remarkably outperforms other meta-heuristics used in this paper. [ABSTRACT FROM AUTHOR]

This paper studies a cascaded flowshop joint scheduling problem that has critical applications in the electronic information equipment manufacturing industry but has received limited attention in the scheduling field. The cascaded flowshop joint scheduling problem encompasses both a distributed permutation flowshop scheduling problem and a hybrid flowshop scheduling problem. This paper investigates the efficient scheduling of a set of jobs in two heterogeneous flowshops to minimize the makespan. We present a mixed integer linear programming mathematical model and a shifting iterated greedy algorithm, which constantly changes its search space to explore different solution spaces. Based on the specific characteristics of the problem, a hybrid scheduling approach that combines forward and backward scheduling, a step-by-step destruction and reconstruction operator, and three adaptive reconstructive methods that combine coarse-tuning and fine-tuning are proposed to explore the near-optimal solution. Through comprehensive computational comparison and statistical analysis, the results demonstrate that the proposed shifting iterated greedy algorithm performs significantly better in relative deviation index values at the same CPU running time. [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]

Intelligent Reflection Surface (IRS) will be widely used in future communication system construction to reduce construction costs and improve coverage. However, IRS systems are generally equipped with controllers to receive wireless signal instructions, this increases the vulnerability of future communications. In this paper, we present an attack tactic to provide a way of thinking for the future defense deployment. At the beginning, the hacker cannot know the whole communication system, then it continuously attacks the IRS system, monitor the communication system, and sequentially learns new information about the system in each attacking round in order to attack more effectively in the next round. A two-layer optimal mathematical model is presented to describe the BS and the hacker's decision. And the two-layer optimization which is difficult to solve is transformed into a single layer linear optimization by using equivalent transformation and dual transformation. A series of mathematical experiments are used to test different scenarios applicable to different monitoring style, and verify that the tactic proposed in this paper can effectively interfere with the system. [ABSTRACT FROM AUTHOR]

Listeriosis is one of the zoonotic diseases affecting most parts of the Sub-Saharan countries. The infection is often transmitted by eating and it can also pass by respiratory and direct contact. In this paper, a listeriosis mathematical model is formulated involving fractal-fractional orders in both Caputo and Atangana–Baleanu derivatives. Moreover, future behaviors of the disease are investigated by considering the fractal–fractional operators that are very effective in modeling the real-life phenomena by virtue of their memory effect. The basic properties and steady states are also obtained. The threshold parameter for determining the spread of the disease is computed. Numerical results are presented for each fractal-fractional-order operator. The results obtained in the paper show that the numerical schemes are effective for predicting and analyzing complex phenomena. [ABSTRACT FROM AUTHOR]

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This paper presents a dynamical analysis of a tethered aerostat using a multibody simulation tool called SimscapeMultibody™. Tethered aerostats are lighter than air vehicles that float in the air and are connected to the ground through the tether. Atmospheric winds are a source of disturbance to the aerostat. The wind produces drag force that causes the aerostat to displace from its mean position. This phenomenon is called blow-by. Solving the mathematical model of the motion dynamics with time step is the conventional method used for simulating the flight dynamics of the aerostat. This paper discusses the application of Simscape Multibody™ for the three-degrees-of-freedom flight dynamic simulation of spherical tethered aerostat. Simscape Multibody™, an extension of MATLAB®/Simulink, is a multibody modeling tool in which physical bodies and their interactions have to be defined to simulate the dynamics. The mathematical model of the defined system is automatically generated by the SimscapeMultibody™ solver, and the system dynamics is simulated. The simulation of dynamics using multibody modeling tools provides high fidelity simulation for the tethered aerostats. [ABSTRACT FROM AUTHOR]

The dynamics of an AUV, which can perform manoeuvres with pitch angles in the range of 90° is investigated in this paper. The purpose of the AUV is to perform a station-keeping manoeuvre at about 90° pitch angle by varying propeller revolution. The AUV is launched in horizontal orientation. Quaternion mathematics, 4 quadrant propeller open water characteristics, and PID controller for propeller revolution are incorporated in manoeuvring mathematical model for this purpose. A procedure for optimizing the gain coefficients for the PID controller is developed using the 7 DoF manoeuvring mathematical model. Two design configurations of the AUV are investigated, positively buoyant and negatively buoyant. The design objective is, the AUV shall travel as far away as possible from the parent vehicle using minimum energy and time. It is shown that both the optimal gain coefficients for the PID controller for propeller revolution and the dynamic response of the AUV is different for each design configuration. The methodology developed in the paper can be used in the design and propeller revolution control system for certain categories of AUVs. The study shows that prime mover torque/ shaft revolution and 7 DoF manoeuvring mathematical model are important in predicting the attitude and trajectory in space and station-keeping manoeuvring capability of an AUV launched from a parent vehicle. [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]

This paper addresses distributed nonlinear model predictive controller design for formation control of agents with fractional-order dynamics (DNMPC-FCFO) in the presence of obstacles. By introducing new constraints, the collisions between non-neighboring agents are avoided while there is no need to use the information of non-neighboring agents. Moreover, by applying contractive constraints in our optimization problem the Lyapunov stability is guaranteed. Since in parallel DMPC method contraction occurred only on first two steps, the use of terminal components that are essential parts of conventional MPC to create stability is eliminated. These components usually complicate the design and are often used for low-end systems. Using fractional-order equations often leads to mathematical models capable of better describing experimental behavior, but due to memory effects, the controller design is usually more complex. The mathematical stability proof is provided in this regard. In the proposed scheme, considering limited communication range in mobile robots, the controller is designed to preserve the network connectivity. Simulation results show the effectiveness of the proposed method. [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]