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Abstract: The flows of paper are analyzed throughout the papermaking processes, with the year 2007 and Korea defined as the system boundaries. In practice, the statistical data on the production, import and export of paper or pulp can be collected with relative ease from the government and industrial associations. However, the input data regarding the volumes of pulp and wastepaper used in different paper products, such as newsprint, printing papers, sanitary and household papers, specialty papers, and corrugating board base, are difficult to obtain because such information is generally kept confidential in the course of corporate operations. The production processes of paper products in Korea are modeled using information on raw materials, their compositions and production yields of products in order to identify and quantify the amounts of pulp and wastepaper used in each paper product. The material flows of paper are then analyzed based on the calculation model derived from the correlation of input and output flows between the individual processes throughout the entire paper lifecycle. Accuracy analysis using both mean absolute error (MAE) and mean absolute percent error (MAPE) is conducted to verify the amounts of pulp and wastepaper calculated from the proposed model against the volumes of domestically consumed pulp and wastepaper provided in the national statistics. Although the calculated values for the past (i.e., the 1980s and 1990s) differ to some degree from the statistical values, the data for the 2000s have a relatively higher level of accuracy, with the MAPE of the total pulp and recycling volume at 5.39% and 5.30%, respectively, thus validating the adequacy of the proposed modeling method. The proposed calculation model can be effectively used in the material flow analysis (MFA) of paper to reduce the burden of data collection and obtain relatively accurate results. [Copyright &y& Elsevier]

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]

Abstract: The non-citation rate refers to the proportion of papers that do not attract any citation over a period of time following their publication. After reviewing all the related papers in Web of Science, Google Scholar and Scopus database, we find the current literature on citation distribution gives more focus on the distribution of the percentages and citations of papers receiving at least one citation, while there are fewer studies on the time-dependent patterns of the percentage of never-cited papers, on what distribution model can fit their time-dependent patterns, as well as on the factors influencing the non-citation rate. Here, we perform an empirical pilot analysis to the time-dependent distribution of the percentages of never-cited papers in a series of different, consecutive citation time windows following their publication in our selected six sample journals, and study the influence of paper length on the chance of papers’ getting cited. Through the above analysis, the following general conclusions are drawn: (1) a three-parameter negative exponential model can well fit time-dependent distribution curve of the percentages of never-cited papers; (2) in the initial citation time window, the percentage of never-cited papers in each journal is very high. However, as the citation time window becomes wider and wider, the percentage of never-cited papers begins to drop rapidly at first, and then drop more slowly, and the total degree of decline for most of journals is very large; (3) when applying the wider citation time windows, the percentage of never-cited papers for each journal begins to approach a stable value, and after that value, there will be very few changes in these stable percentages, unless we meet a large amount of “Sleeping Beauties” type papers; (4) the length of an paper has a great influence on whether it will be cited or not. [Copyright &y& Elsevier]

Abstract: A lot of work has been done in the field of multi-valued model checking home and abroad. But some research works are not perfect. Some problems in the semantic definitions of temporal operators and corresponding model checking algorithms are found in the paper “Model Checking Using Partial Kripke Structure with 3-Valued Temporal Logic”. Some counterexamples are given to show these errors. On the basis of the discussion, some sound semantic definitions of temporal operators and correct algorithms of model checking for some temporal operators are given. [Copyright &y& Elsevier]

Abstract: Although the scale invariance is a main feature of growing networks, evidence from a few modelled networks show that the finite-size effect of network at a fixed time cannot be ignored. We propose a concept of time-dependent scale-free networks, and prove the criteria for the stability and scale-free of growing networks by the degree-growing Markov chain. Our results show the importance of rigorous theoretical analysis for the network science. [ABSTRACT FROM AUTHOR]

The last-mile delivery challenges and complexity are increasing simultaneously with the e-commerce growth, the e-commerce growth constitutes a threat to urban transport, especially for big cities and developing countries. This paper examines the economic impact of crowd-shipping based on public transport collaborated with the automated parcel locker and the impact of traditional delivery, as two different methods of last-mile delivery under the same circumstances. This paper aims to assess crowd-shipping as an alternative solution for last-mile delivery compared to traditional delivery. Data has been simulated based on Cairo reality, one of the largest cities in the world. The mathematical models were formulated for the problem, where the first model objective function minimizes the costs of vehicle possession and vehicle kilometers traveled, while the second model objective function minimizes the costs of establishing lockers, vehicle possession, and vehicle kilometers traveled. A genetic algorithm has been used due to the complexity of the problem. The results show that the economic impact of the crowd-shipping delivery system is better than the traditional delivery system under the conditions of large-sized and medium-sized demand, while the economic impact of traditional delivery is better than the crowd-shipping delivery system under the conditions of small-sized demand. [ABSTRACT FROM AUTHOR]

This paper proposes a model to predict the tensile characteristics of metal fused filament fabricated (MFFF) components. The proposed model consists of mathematical, experimental, and finite element (FE) models. The mathematical model was constructed based on the composite laminate theory and was combined with experiments for basic layup of 0° and 90° raster angle to describe the behavior of MFFF parts. The FE model was built to simulate the behavior of MFFF parts in a virtual environment and its validity was verified using independent experiments for a more common layup of +45°/−45°. • Predicts the mechanical properties of a MFFF specimen printed with antisymmetric layup. • Models the anisotropic material behavior of MFFF parts using classic laminate theory. • Introduces the procedure for fabricating defect free metal FFF sample. • Investigates the effect of FFF process parameters on the quality of metal parts. [ABSTRACT FROM AUTHOR]

The application of optimal control theory in practical engineering is often limited by the modeling cost and complexity of the mathematical model of the controlled plant, and various constraints. To bridge the gap between the theory and practice, this paper proposes a model-free direct method based on the sequential sampling and updating of surrogate model, and extends the ability of direct method to solve model-free optimal control problems with general constraints. The algorithm selects sample points from the current actual trajectory data to update the surrogate model of controlled plant, and solve the optimal control problem of the constantly refined surrogate model until the result converges. The presented initial and subsequent sampling strategies eliminate the dependence on the model. Furthermore, the new stopping criteria ensure the overlap of final actual and planned trajectories. The several examples illustrate that the presented algorithm can obtain constrained solutions with greater accuracy and require fewer sample data. • The proposed sampling strategies do not depend on the models of controlled plant. • New stopping criteria ensure the overlap of final actual and planned trajectories. • Model-free direct method can deal with constrained optimal control problems well. [ABSTRACT FROM AUTHOR]

Energy efficient permutation flowshop scheduling problem (PFSP) is widely studied to reduce the energy consumption in manufacturing systems. Most traditional studies make the premise that setup time is a component of processing time. However, this assumption does not satisfy the small batches, more varieties manufacturing models criterion. Therefore, energy efficient PFSP with setup times thus becomes a pressing problem that needs to be resolved. The contribution of this paper includes: (1) a permutation flowshop scheduling problem (PFSP) mathematical model by considering energy consumed and setup time by each machine in the system; (2) a grey-wolf algorithm optimization with variable neighborhood search (VNS) method (GWO_VNS) is proposed for the multi-objective PFSP model; (3) solved 9 benchmarks problems of Taillard (1993) [8] for the minimization of flowtime (FT) and energy consumption (EC). The performance of the proposed GWO_VNS algorithm is evaluated on the benchmark problems selected from the published literature [15]. It is noted that the proposed algorithm performed better on both the objectives i.e., FT and EC minimization. Overall, the proposed algorithm achieved 33.25% and 30.56% average improvement in FT and EC minimization respectively on benchmark problems. [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]

In-band network telemetry is a powerful framework for network monitoring. It allows the collection of telemetry data in real-time and provides network-wide visibility. However, depending on the routing of network flows and which telemetry data are collected, the network-wide visibility and the performance of monitoring applications may decrease. In this paper, we present the in-band network telemetry problem and extend the existing mathematical optimization models of the problem by proposing a new model that computes the routing of network flows. Results show that the new model outperforms existing models in term of network coverage and monitoring applications performance. The results of this work can be useful for network managers and enterprises to gain real-time insights into network performance. [ABSTRACT FROM AUTHOR]

Over the last decades increased use of waste paper as a source of fibre for the pulp and paper production process has meant that the industry has undergone significant changes in material and energy use. However, this means use of technologies that do not generate a significant amount of biomass for energy recovery, and thus requires that more energy is purchased by the industry. If waste paper is incinerated instead of repulped, energy purchases by society can be reduced, which will have a positive effect on CO2 emissions. In this article, we argue that an analysis of these effects requires a systems analytical approach including the different production lines, fibre flows and alternative uses of the fibre rather than a life cycle analysis with allocation methods. In the latter case, one often looks at just one production process and uses allocation methods for in- and outflow from or to other processes. We show that allocation methods sometimes used in life cycle analyses do not give a good approximation. Thus, it is recommended that allocation be avoided by, for example, expanding the system. If allocation cannot be avoided, the allocation should be based on the way in which the inputs and outputs are changed by quantitative changes in the products or functions delivered by the system. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of optimizing energy efficiency in production shop scheduling problem, focusing specifically on the constraint of sequence-dependent setup times (FSSP SDST). This study is conducted in the context of promoting green industrial practices. The main objective is to minimize total energy consumption (TEC) in the manufacturing process. We introduce a mathematical model of this problem. To solve this problem, we use the Artificial Bee Colony algorithm (ABC). In addition, the Migrating Birds Optimization (MBO) algorithm will be presented. The effectiveness of the proposed algorithms will be subjected to rigorous evaluation through detailed tests on different instances. This methodical approach will enable us to carry out an in-depth and detailed comparative analysis, highlighting the relative strengths and weaknesses of the algorithms presented in this study. [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]

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]

Current production structures require large amounts of primary materials and are not likely to be sustained without large implications for the environment. A good understanding of societal metabolism is likely to contribute to more sustainable production and consumption. Material flow analysis (MFA) intends to support this understanding by providing insight in material flows. In this article a new method for analyzing materials flows, called STREAMS, is tested. The method is applied to analyze the paper and wood flow through the economic system of The Netherlands. The method is based on data available from the so-called supply and use tables; these tables are made available by Statistics Netherlands and describe the economy of a country in terms ofannual supply and use of goods and services by industries and consumers. The method proves to be very useful in analyzing the paper and wood flow in The Netherlands. The method provides detailed informationabout the final consumption of paper and wood, even for packaging materials and product parts made out of paper and wood. Trends are visible that statistical offices collect less physical data about material flows. This will make the construction of material flow analyses like this one more difficult in the future. [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]

Risks are uncertain events that can affect criteria such as project cost, quality, and completion time and ultimately lead to project failure. That is why the project risk management process, which is one of the most fundamental parts of project management, must be done properly. In this paper, a new multi-objective fuzzy model is proposed to respond appropriately to the primary and secondary project risks. The contributions, such as risk interdependency, considering the project with multi-mode activities, resource considerations, as well as attention to interval-valued fuzzy uncertainties are regarded simultaneously for the first time. The purpose of the proposed multi-objective mathematical model is to minimize cost, quality reduction, and project completion time. Then, a new two-stage uncertain solution approach is introduced in this paper. In this solution approach, using an equivalent method in the first step and then using an extended multi-choice goal programming method in the second step, for both lower and upper limits of the membership functions for interval-valued fuzzy numbers, the computational processes are performed. To show the application and effectiveness of the proposed model, a case study adapted from the literature is given. Finally, sensitivity analysis is conducted to analyze the behavior of the developed mixed-integer programming model. The analysis of obtained results indicates that the utilization of the proposed model leads to better and more appropriate solutions. • Presenting a new multi-objective programming model for project risk responses. • Proposing multi-mode activity and resource considerations in project risk modeling. • Introducing a new interval-valued fuzzy solution approach to handle uncertainty. • Focusing on primary and secondary risks along with project risk interdependency. • Implementing the proposed model in a case study for electricity industry projects. [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]

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]

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]

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]

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]

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]

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]

Renewable powered dc microgrid is more cost-effective, simple, reliable, highly efficient, and scalable than the traditional electric grid. This paper provides an overview of high gain dc converter topologies for dc microgrids. This research's primary objective is to impart a unique extensive, comparative, and perceptive analysis in high gain converter configurations. The most recently reported research articles are considered for discussion in this paper. Initially, based on the structural configurations, the converters are segregated and are grouped into four categories, such as Replacement, Reduced Redundant Power Processing (R2P2), Incorporation, Integration (R2I2) techniques. Secondly, based on the dynamic and steady-state analysis, the topological converter configurations are comparatively analyzed. A few topologies' mathematical modeling is also discussed to obtain a comprehensive insight into the converter's dynamic behavior. An extensive analysis, comparisons, and assessment of these converters with diverse configurations are acquainted with tables and figures are presented for choosing an appropriate converter suitable for dc grid application. The pros and cons of the topology derivation under each categorization are presented for benefitting the researchers in selecting the topological configuration method. In the end, the comparison results of the high gain converter clusters are summarized, and recommendations are given, which leads to the guidelines for selecting a significant high gain topology for a specific application. Furthermore, the constraints considered for topology derivation with the type of analysis carried out are presented. This gives a way to find the scope for future research under this area which is finally offered. This study will also provide an insight into the derivation of several configurations of the novel high gain dc-dc converter in the field of dc microgrid. [ABSTRACT FROM AUTHOR]

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]

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]

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]

The problem of motion of coupled harmonic oscillators can be modeled in the terms of system of ordinary differential equations (SODEs). In the present paper, authors used Sawi transformation for finding the solution of SODEs with application to determine the concentration of chemical reactants of chemical reaction in series. Authors have considered four numerical problems and determined their solutions step by step for explaining the complete method. After it, authors have taken a problem of physical chemistry for determining the concentration of chemical reactants (substances) of chemical reaction in series. Authors have obtained the solution of this problem by developing its mathematical model in terms of SODEs and then applying Sawi transformation on it. Results of this problem are visualized using tables and graphs. Results of the present paper show that the Sawi transformation provides us the exact solution of SODEs without large computational work. [ABSTRACT FROM AUTHOR]

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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]

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]

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. 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.)

Quality of life indicators include measures of both objective and subjective well-being to broaden the traditional income-focused view towards a multidimensional view of welfare. In this paper, we develop a single reliable composite index employing fuzzy mathematical modelling that considers the effect of uncertainty and imprecision related to the specific nature of the phenomenon being measured. Additionally, we suggest a procedure for incorporating this new way of assigning priorities through multicriteria techniques. To this end, we propose a new multicriteria approach that introduces the recent notion of picture fuzzy sets to deal with uncertainty and imprecision in the assessment of objective and subjective dimensions of quality-of-life performance. To verify the effectiveness of the whole approach and the credibility of the results, we show its application for monitoring the quality of life in the European Union. Our results show that fuzzy numbers can be a useful tool when combined with multicriteria techniques in the prioritization phase of the criteria and that they increase the quality and reliability of the overall aggregate indicator. Furthermore, through statistical measures we found that in a picture environment the results are consistent regardless of the multi-criteria approach used to provide a final classification. • Quality of life indicators improve traditional quantitative economic performance measures. • Objective and subjective criteria enrich the construction of the quality of live composite indices. • Picture fuzzy sets and multicriteria approaches are applied to deal with objective and subjective criteria. • A robust picture of quality of life performance for EU countries is provided. • The picture fuzzy multicriteria approach improves confidence on the achieved results. [ABSTRACT FROM AUTHOR]

• Providing a comprehensive examination of the electric bus scheduling problem. • Elaborating charge scheduling within the context of electric bus scheduling challenges. • Presenting a detailed exploration of mathematical models considering different constraints and objective functions. • Discussing diverse solution approaches tailored to different objective functions. • Proposing the literature gaps and future research. Public transportation is in a new era of electrification, with battery-powered electric buses emerging for sustainable urban mobility. However, their operational characteristics, including prolonged charging times and limited travel distances, have introduced complex scheduling challenges that demand innovative solutions. This review paper explores the electric bus scheduling problem (EBSP), comprehensively analyzing state-of-the-art research in this rapidly evolving field. The literature is categorized based on structural and procedural aspects. In the structural section, the different structures of defining EBSP have been explained. Moreover, mathematical models, their associated constraints, and objective functions are examined. The procedural section reviews various solution methodologies based on the underlying objective functions. The review reveals potential avenues for future research, such as investigating multi-depot and multi-vehicle configurations, incorporating charge scheduling and partial charging, developing robust schedules to account for uncertainty, and applying advanced techniques like machine learning and hyper-heuristics to optimize electric bus transportation efficiency. [ABSTRACT FROM AUTHOR]

Most existing studies on distribution network reconfiguration (DNRC) are predominantly based on a fixed initial topology and optimize switch operations to achieve various objectives, without considering the flexible allocation of backup lines. This paper proposes a novel problem of optimizing the reconfiguration capability of distribution networks (DNs) by considering the expansion of power lines under resource constraints. The reconfiguration capability reflects the ability of a DN to respond to uncertainties and disturbances by adjusting its topology. A novel metric based on the number of spanning trees is proposed to quantify the reconfiguration capability of a DN. Moreover, an optimization model is formulated to maximize the reconfiguration capability of a DN subject to resource constraints on line expansions. To solve this model efficiently, a multifactorial evolutionary algorithm (MFEA) is developed, which can optimize multiple tasks with different expansion line quantities simultaneously by exploiting knowledge transfer across tasks. The proposed metric, model, and algorithm are validated on two case studies using the IEEE 33-bus and 70-bus test systems, and the results show their effectiveness and superiority over existing methods. • Proposed a novel metric for distribution network reconfiguration capability based on spanning trees. • Formulated a mathematical model to maximize the distribution network reconfiguration capability. • Developed a multifactorial evolutionary algorithm for the efficient solution. • Results demonstrate the effectiveness and superiority of the proposed metric, model, and algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with a control problem of discrete event systems subject to capacity constraints. Combined use of timed event graphs and Min-Plus algebra is a well-known approach and efficient for handling timed behavior and mathematical modelling of discrete event systems. However, in current literature, many of control approaches assume that system states are fully observable, which is not the case in our study. Hence, we propose in this paper a feedback control method to guarantee the respect of marking constraints imposed for some paths of partially observable timed event graphs. We demonstrate that if each loop of the considered TEG contains at last one observable transition, we can derive a realisable control law satisfying a set of constraints. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive direct power control (MP-DPC) with power compensation and circulating current reduction is proposed for modular multilevel converter under unbalanced grid conditions. The main objectives of this paper are to achieve the circulating currents reduction and the power ripples elimination under unbalanced grid-side voltage conditions. In order to realize the aforementioned objectives, a triple-stage MP-DPC method based on a decoupled mathematical model of circulating current and DC-link current components is presented, which results in the elimination of weighting factors. Meanwhile, a power compensation term is embedded in the proposed design so as to eliminate the power ripples. In this sense, the power compensation terms are expressed by grid voltages and their quadrature signals without the complex positive/negative sequences extraction of grid voltage/currents and phase-locked loop. The proposed methodology can not only relieve the effect of circulating currents but also eliminate the active and reactive power ripples while remaining computationally feasible under unbalanced grid conditions. Additionally, a reduced switching frequency approach is introduced to decrease the power loss. Finally, the effectiveness of the proposed MP-DPC method is validated by both simulation and experimental results. • A triple-stage FCS-MPC method is proposed to regulate multiple control objectives of MMC, which results in the elimination of weighting factors. • To enhance the grid-side power quality, the proposed strategy takes power compensation scheme into consideration under unbalanced grid-side conditions. • Compared with the conventional MP-DPC method, the circulating current and switching frequency are restrained in this design while not punishing the computational complexity. [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]

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 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]

In this paper, the application of spatial partial differential equation in user intent oriented information mining is proposed. The hybrid mining algorithm is used to avoid the defects of a single algorithm, which makes the discovery and identification of user intent efficient and practical. The v-svc algorithm can suppress the classification of errors, the hierarchical generation based on attributes can easily get the focus of services, and the fuzzy clustering method can get the service level or service quality of users. This method can be used for any complex object research similar to user intention and the evaluation mechanism of a research object. In view of the problems of traditional methods, this paper puts forward the technology of association mining based on the mathematical model of partial differential classification, and makes an experimental comparative analysis. The results show that the convergence, stability and performance of this method are good. [ABSTRACT FROM AUTHOR]

Three-phase PWM rectifiers are adopted extensively in industry because of their excellent properties and potential advantages. However, while the IGBT has an open-circuit fault, the system does not crash suddenly, the performance will be reduced for instance voltages fluctuation and current harmonics. A fault diagnosis method based on deep feedforward network with transient synthetic features is proposed to reduce the dependence on the fault mathematical models in this paper, which mainly uses the transient phase current to train the deep feedforward network classifier. Firstly, the features of fault phase current are analyzed in this paper. Secondly, the historical fault data after feature synthesis is employed to train the deep feedforward network classifier, and the average fault diagnosis accuracy can reach 97.85% for transient synthetic fault data, the classifier trained by the transient synthetic features obtained more than 1% gain in performance compared with original transient features. Finally, the online fault diagnosis experiments show that the method can accurately locate the fault IGBTs, and the final diagnosis result is determined by multiple groups results, which has the ability to increase the accuracy and reliability of the diagnosis results. [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]