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We consider a mathematical model which describes the equilibrium of two elastic membranes fixed on their boundary and attached to an adhesive body, say a glue. The variational formulation of the model is in a form of an elliptic quasivariational inequality for the displacement field. We prove the unique weak solvability of the model, and then we state and prove a convergence result, for which we provide the corresponding mechanical interpretation. Next, we consider two associated optimization problems for which we provide existence results. Finally, we the present numerical simulation which validates our convergence result. We end this paper with some concluding remarks and an Appendix, in which we present the preliminary material needed in this paper. [ABSTRACT FROM AUTHOR]

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. [ABSTRACT FROM AUTHOR]

Near-field (NF) integrated sensing and communication (ISAC) has the potential to revolutionize future wireless networks. It enables simultaneous communication and sensing operations on the same radio frequency (RF) resources using a shared hardware platform, maximizing resource utilization. NF-ISAC systems can improve communication and sensing performance compared to traditional far-field (FF) ISAC systems by exploiting the unique propagation characteristics of NF spherical waves with an additional distance dimension. Despite its potential, NF-ISAC research is still in its early stages, and a comprehensive survey of the technology is lacking. This paper systematically explores NF-ISAC technology, providing an in-depth analysis of both NF and FF systems, their applicability in various scenarios, and different channel models. It highlights the advantages and philosophies of ISAC, examining both narrow-band and wide-band NF-ISAC systems. Case studies and simulations offer deeper insights into NF-ISAC design philosophies. Additionally, the paper reviews the existing NF-ISAC literature, methodologies, potentials, and conclusions, and discusses future research areas, challenges, and applications. [ABSTRACT FROM AUTHOR]

In this paper, based on the framework of the supplementary variable method, we present two classes of high-order, linearized, structure-preserving algorithms for simulating the regularized long-wave equation. The suggested schemes are as accurate and efficient as the recently proposed schemes in Jiang et al. (2022) [20] , but share the nice features in two folds: (i) the first type of schemes conserves the original energy conservation, as opposed to a modified quadratic energy in [20] ; (ii) the second type of schemes fills the gap of [20] by constructing high-order linear algorithms that preserve both two invariants of mass and momentum. We discretize the SVM systems by employing the algebraically stable Runge-Kutta method together with the prediction-correction technique in time and the Fourier pseudo-spectral method in space. The implementation benefits from solving the optimization problems subject to PDE constraints. Numerical examples and some comparisons are provided to show the effectiveness, accuracy and performance of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we studied the AA-iterative algorithm for finding fixed points of the class of nonlinear generalized (α, β)-nonexpansive mappings. First, we proved weak convergence and then proved several strong convergence results of the scheme in a ground setting of uniformly convex Banach spaces. We gave a few numerical examples of generalized (α, β)-nonexpansive mappings to illustrate the major outcomes. One example was constructed over a subset of a real line while the other one was on the two dimensional space with a taxicab norm. We considered both these examples in our numerical computations to show that our iterative algorithm was more effective in the rate of convergence corresponding to other fixed point algorithms of the literature. Some 2D and 3D graphs were obtained that supported graphically our results and claims. As applications of our major results, we solved a class of fractional differential equations, 2D Voltera differential equation, and a convex minimization problem. Our findings improved and extended the corresponding results of the current literature [ABSTRACT FROM AUTHOR]

In this article, the authors present some results related to the search for an optimal solution to the optimization problem of managing an economic system with a finite number of interconnected institutions that are influenced by an external “regulator”. The regulator is interested in the fruitful interaction of all structures; it manages the system by setting appropriate priorities. The optimization problem comes down to finding the maximum point of the objective function of the regulator (optimizer). The paper presents an analytical solution to the problem and derives formulas for finding the maximum point of the objective function for the case of a finite number of deterministic priorities. [ABSTRACT FROM AUTHOR]

A node of a Sturm–Liouville problem is an interior zero of an eigenfunction. The aim of this paper is to present a simple and new proof of the result on sharp bounds of the node for the Sturm–Liouville equation with the Dirichlet boundary condition when the L 1 norm of potentials is given. Based on the outer approximation method, we will reduce this infinite-dimensional optimization problem to the finite-dimensional optimization problem. [ABSTRACT FROM AUTHOR]

In this work we focus on the development of a numerical algorithm for the inverse elastography problem. The goal is to perform an efficient material parameter identification knowing the elastic displacement field induced by a mechanical load. We propose to define the inverse problem through a quadratic optimization program which uses the direct problem formulation to define the objective function. In this way, we end up with a convex minimization problem which attains its minimum at the solution of a linear system. The effectiveness of our method is illustrated through numeral examples. [ABSTRACT FROM AUTHOR]

The question "How does it work" has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of computing intensive tasks has led to an overload of SMD (Smart Mobile Devices) computing tasks. By using MEC(Mobile Edge Computing Servers) and idle ED (Edge Devices) in the network, SMD with limited computing power can offload computing tasks to MEC and ED collaboration, and enhance system security based on the DPoR (Delegated Proof of Reputation) consensus mechanism. This study proposes a blockchain mobile edge computing offloading model based on BS AFSA (Bird Swarm Artificial Fish Swarm Algorithm), which transforms the task offloading problem into an optimization objective function to reduce the computational overhead. The improved BS AFSA is used to optimize the task delay and energy consumption, and the behavior parameters in the algorithm are constructed and the crowding factor is improved to elevate the local search accuracy in the later iteration. The simulation results show that compared with other benchmark algorithms, the proposed algorithm reduces the possibility of falling into local optimum and effectively reduces the total system cost of the joint offloading scheme. [ABSTRACT FROM AUTHOR]

We propose a method to control the motion of a capsule endoscope (CE) in the stomach utilizing either a single external permanent magnet (EPM) or dual EPMs to extend the examination of the upper gastrointestinal tract. When utilizing the conventional magnetic navigational system (MNS) with a single EPM to generate tilting and rotational motions of the CE, undesired translational motion of the CE may prevent accurate examination. We analyzed the motion of the CE by calculating the magnetic torque and magnetic force applied to the CE using the point-dipole approximation model. Using the proposed model, we propose a method to determine the optimal position and orientation of the EPM to generate tilting and rotational motions without undesired translational motion of the CE. Furthermore, we optimized the weight of dual EPMs to develop a lightweight MNS. We prototyped the proposed MNS and experimentally verified that the developed MNS can generate tilting and rotational motions of the CE without any translational motion. [ABSTRACT FROM AUTHOR]

A heuristic approach to optimization of complex physicochemical processes in the form of a genetic algorithm for solving problems is presented. In comparison with other evolutionary methods, the genetic algorithm allows working with large search spaces and complex evaluation functions, which is especially important in the study of multifactor physicochemical systems. Due to the relatively high need for computing resources when working with large and complex search spaces, optimization of existing calculation organization schemes has a positive effect on the accuracy of the calculated results. The paper presents a modified genetic algorithm that minimizes the number of iterations to achieve a given accuracy when solving the problem of finding the optimal composition of the initial reaction mixture. For a complex physicochemical process, an optimization problem is formulated which consists in finding the composition of the initial reaction mixture that promotes maximization (or minimization) of a given target parameter. The optimality criterion is determined by the type of the problem being solved and, when organizing calculations, is focused on the maximum yield of the target product. The main steps of implementing the genetic algorithm include creating an initial set of solutions and subsequent iterative evaluation of their quality for subsequent combination and modification until optimal values are achieved using mechanisms similar to biological evolution. To improve the efficiency of the method and reduce the number of iterations, a modification of the genetic algorithm is proposed which boils down to a dynamic estimate of the “mutation” parameter, depending on the diversity of individuals in the formed population of solutions. In a series of computational experiments, an analysis was made of the influence of the genetic algorithm parameters on the accuracy and efficiency of solving the problem using the example of studying the kinetics of the Michaelis-Menten enzymatic reaction. The results of calculations to determine the optimal composition of the reaction mixture showed that the dynamic determination of the “mutation” parameter contributes to an increase in the accuracy of the problem solution and a multiple decrease in the relative error value reaching 0.77 % when performing 200 iterations and 0.21 % when performing 400 iterations. The presented modified approach to solving the optimization problem is not limited by the type and content of the studied physicochemical process. The calculations performed showed a high degree of influence of the “mutation” parameter on the accuracy and efficiency of the problem solution, and dynamic control of the value of this parameter allowed increasing the speed of the genetic algorithm and reduce the number of iterations to achieve an optimal solution of a given accuracy. This is especially relevant in the study of multifactorial systems when the influence of parameters is non-trivial.

In this paper, we studied the AA-iterative algorithm for finding fixed points of the class of nonlinear generalized $ (\alpha, \beta) $-nonexpansive mappings. First, we proved weak convergence and then proved several strong convergence results of the scheme in a ground setting of uniformly convex Banach spaces. We gave a few numerical examples of generalized $ (\alpha, \beta) $-nonexpansive mappings to illustrate the major outcomes. One example was constructed over a subset of a real line while the other one was on the two dimensional space with a taxicab norm. We considered both these examples in our numerical computations to show that our iterative algorithm was more effective in the rate of convergence corresponding to other fixed point algorithms of the literature. Some 2D and 3D graphs were obtained that supported graphically our results and claims. As applications of our major results, we solved a class of fractional differential equations, 2D Voltera differential equation, and a convex minimization problem. Our findings improved and extended the corresponding results of the current literature.

Abstract In this work we focus on the development of a numerical algorithm for the inverse elastography problem. The goal is to perform an efficient material parameter identification knowing the elastic displacement field induced by a mechanical load. We propose to define the inverse problem through a quadratic optimization program which uses the direct problem formulation to define the objective function. In this way, we end up with a convex minimization problem which attains its minimum at the solution of a linear system. The effectiveness of our method is illustrated through numeral examples.

Given a directed graph G = (V , A) , we tackle the Minimum Feedback Arc Set (MFAS) Problem by designing an efficient algorithm to search for minimal and stable Feedback Arc Sets, i.e. such that none of the arcs can be reintroduced in the graph without disrupting acyclicity and such that for each vertex the number of eliminated outgoing (resp. incoming) arcs is not bigger than the number of remaining incoming (resp. outgoing) arcs. Our algorithm has a good polynomial upper bound and can therefore be applied even on large graphs. We also introduce an algorithm to generate strongly connected graphs with a known upper bound on their feedback arc set, and on such graphs we test our algorithm. [ABSTRACT FROM AUTHOR]

This paper studies some results on solutions associated with interval-valued optimal control problems driven by generalized invariant convex (for short, invex) functionals. Necessary conditions of optimality are stated for the considered optimization problem. Also, the sufficiency of the necessary optimality conditions is investigated. Moreover, various duality relationships between the dual models and the primal optimization problem are deliberated. [ABSTRACT FROM AUTHOR]

Applying a parametric approach, in this paper we studied a new class of multidimensional extremization models with data uncertainty. Concretely, first, we derived the robust conditions of necessary optimality. Thereafter, we established robust sufficient optimality conditions by employing the various forms of convexity of the considered functionals. In addition, we formulated an illustrative example to validate the theoretical results.

This article explores the optimization of global navigation satellite system (GNSS) acquisition using metaheuristic techniques. It focuses on improving the detection performance of the GNSS acquisition system by optimizing the cell averaging constant false alarm rate (CA-CFAR) thresholding in Rayleigh fading channels. The study suggests the use of metaheuristic optimization algorithms such as particle swarm optimization (PSO), biogeography-based optimization (BBO), firefly algorithm (FA), and simulated annealing (SA). The results demonstrate that the optimized thresholds have a significant impact on the system's performance. The article also discusses the use of the constant false alarm rate (CFAR) technique, which utilizes two CA-CFAR detectors to estimate the noise power level and adaptively set a threshold for detection. The detection performance can be further improved by combining the outputs of the two detectors using fusion rules. The article presents and compares the results of simulations and theoretical analysis for different optimization algorithms, showing that the CA-CFAR detector with optimization outperforms the fixed threshold detector and the CA-CFAR detector without optimization. The best results are achieved when two different detectors are used with the CA-CFAR detector and the fusion rule is applied. The study suggests further research in non-homogeneous environments and the exploration of other types of detectors with new optimization methods. [Extracted from the article]

Chebyshev polynomials of the first kind are applied to construct the generalized Chebyshev–Poisson integral. The optimization problem for the generalized Chebyshev–Poisson operator as a functional of a function defined on an interval is solved, and its approximate properties on Holder classes H1 are analyzed. An exact equality is obtained for the deviation of Hölder class functions from the generalized Chebyshev–Poisson integral. [ABSTRACT FROM AUTHOR]

As the real estate industry expands with time, the personalized needs of users for indoor space layouts have become increasingly complex. Traditional indoor space layout design methods can no longer meet the needs of large market groups because of their complex steps and low levels of specialization. Therefore, this study first analyzes the problematic factors in indoor space layout design. Second, an interactive genetic algorithm is introduced to solve the multifactor optimal selection problem; the process is optimized and improved using a differential evolution algorithm. A comprehensive spatial layout model combining interactive genetic and differential evolution algorithms is proposed. The experimental results show that the model performs best with uniform variation, and its average number of iterations to find the optimal individual is 57. In addition, compared with similar layout models, the proposed model achieved the highest space utilization value of 79%, which is approximately 19% higher than that for the stacking layout model; it also required the shortest time, that is, 15 min. In summary, the proposed model provides a new intelligent method for indoor layout design, which is expected to improve the satisfaction of designers and users. [ABSTRACT FROM AUTHOR]

Applying a parametric approach, in this paper we studied a new class of multidimensional extremization models with data uncertainty. Concretely, first, we derived the robust conditions of necessary optimality. Thereafter, we established robust sufficient optimality conditions by employing the various forms of convexity of the considered functionals. In addition, we formulated an illustrative example to validate the theoretical results. [ABSTRACT FROM AUTHOR]

Chebyshev polynomials of the first kind are used to construct the generalized Chebyshev–Poisson integral. The optimization problem for the generalized Chebyshev–Poisson operator as a functional of a function defined on a segment is solved, and its approximate properties on Holder classes H¹ are analyzed. An exact equality is obtained for the deviation of Holder class functions from the generalized Chebyshev–Poisson integral. [ABSTRACT FROM AUTHOR]

The article considers the problem of reducing the losses caused by the implementation of threats to the topology of connections. Threats considered may relate to the integrity, confidentiality and availability of the information transmitted by the corresponding connection. At the same time, it is assumed that the amount of total funding allocated to protect against these threats is limited to a certain amount. This amount should be divided into parts, each of which will correspond to the financing of protection against a certain threat. A corresponding mathematical model was created to solve this problem. In this model, we make the reasonable assumption that the more funding is provided to protect against a threat, the less is the probability of its occuring. With this assumption, the problem is reduced to an optimization problem, which, generally speaking, cannot be solved by analytical methods. But for a small number of variables (up to 100 variables), this problem can be solved numerically using the tools of the Mathematica package. The article also provides the program code that implements the solution of this problem, and numerical examples of its solution using this code. [ABSTRACT FROM AUTHOR]

Optimizing the architecture of superconducting quantum processors is crucial for improving the efficiency of executing quantum programs. Existing schemes either modify general-purpose architectures, which might lead to an increase in the probability of qubit frequency collisions, or customize special-purpose architectures based on the quantum programs to reduce the gate operations after qubit mapping, but the architectures lack support for the post-mapping gate operations' optimization of multiple programs, which reduce their reusability. In this study, we propose a new processor architecture design method that reduces the average growth of the total post-mapping gate count on multiple quantum programs as well as to reduce the impact of processor architecture on frequency collisions, and thus improve the reusability of special-purpose processor. The main idea is to construct a new architecture by finding maximum common edge subgraph among multiple special-purpose processor architectures. To show the effectiveness of our method, we selected quantum programs with different functions covering 9 types of qubit numbers for comparison. Comprehensive simulation results show that the architecture schemes generated by using our method outperform two general-purpose architecture schemes based on the square lattice and the eff-5-freq's special-purpose architecture schemes, respectively. Compared to the all 2-qubit bus and the eff-5-freq's architecture schemes, after qubit mapping, the architecture schemes of our method have the smallest average growth of gate operations in multiple quantum programs (the largest average growth is 5.63%), which further supports the execution of different quantum programs. Meanwhile, the architecture schemes of our method also reduce the probability of frequency collisions by at least 4.48% compared to all other schemes. Furthermore, we compared our method with another special-purpose design method. In the schemes of different special-purpose architecture design methods, our method is able to generate architectures with better matching for multiple quantum programs. Therefore, our method can provide superconducting quantum processor architecture design with higher reusability for multiple quantum programs. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA) and multi-verse optimizer (MVO) are the recognized optimization strategies frequently employed in numerous scientific areas. However, both SCA and MVO grapple with optimizing the transition between the exploitation and exploration mechanisms. Furthermore, MVO exhibits constraints in its exploitation capabilities. To tackle these limitations, this paper introduces a hybrid model termed SMVO, combining the advantages of both SCA and MVO. This hybrid approach seeks to harmonize exploitation and exploration stages by leveraging the unique advantages of each parent algorithm. The efficacy of SMVO was assessed using 23 benchmark test functions, revealing its competitive performance against not only SCA and MVO but also the ant lion optimization (ALO) and the dragonfly algorithm (DA). Additionally, SMVO's applicability was further validated by successfully addressing three distinct engineering optimization challenges, underscoring its stability and promise as a global optimization tool. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency. [ABSTRACT FROM AUTHOR]

Humans spend a significant portion of their time in the state of sleep, and therefore one's'sleep health' is an important indicator of the overall health of an individual. Non‐invasive methods such as electroencephalography (EEG) are used to evaluate the 'sleep health' as well as associated disorders such as nocturnal front lobe epilepsy, insomnia, and narcolepsy. A long‐duration and repetitive activity, known as a cyclic alternating pattern (CAP), is observed in the EEG waveforms which reflect the cortical electrical activity during non‐rapid eye movement (NREM) sleep. The CAP sequences involve various, continuing periods of phasic activation (phase‐A) and deactivation (phase‐B). The manual analysis of these signals performed by clinicians are prone to errors, and may lead to the wrong diagnosis. Hence, automated systems that can classify the two phases (viz. Phase A and Phase B accurately can eliminate any human involvement in the diagnosis. The pivotal aim of this study is to evaluate the usefulness of stopband energy minimized biorthogonal wavelet filter bank (BOWFB) based entropy features in the identification of CAP phases. We have employed entropy features obtained from six wavelet subbands of EEG signals to develop a machine learning (ML) based model using various supervised ML algorithms. The proposed model by us yielded an average classification accuracy of 74.40% with 10% hold‐out validation with the balanced dataset, and maximum accuracy of 87.83% with the unbalanced dataset using ensemble bagged tree classifier. The developed expert system can assist the medical practitioners to assess the person's cerebral activity and quality of sleep accurately. [ABSTRACT FROM AUTHOR]

The author considers an optimization problem for the triharmonic equation under specific boundary conditions. As a result, the triharmonic Poisson integral is constructed in Cartesian coordinates for the upper half-plane. The asymptotic properties of this operator on Lipschitz classes in a uniform metric are analyzed. An exact equality is found for the upper bound of the deviation of the Lipschitz class functions from the triharmonic Poisson integral defined in Cartesian coordinates for the upper half-plane in the metric of space C. The results obtained in the article demonstrate how the methods of approximation theory relate to the principles of the optimal decision theory. [ABSTRACT FROM AUTHOR]

The author considers the optimization problem for the triharmonic equation in the presence of specific boundary conditions. As a result, the triharmonic Poisson integral was constructed in Cartesian coordinates for the upper half-plane. The asymptotic properties of this operator on Lipschitz classes in a uniform metric were studied. An exact equality was found for the upper bound of the deviation of the Lipschitz class functions from the triharmonic Poisson integral defined in Cartesian coordinates for the upper half-plane in the metric space. The results obtained in the article demonstrate the connection between the methods of approximation theory and the principles of optimal decision theory. [ABSTRACT FROM AUTHOR]

Optimization of the “mature” fields development in machine learning algorithms is one of the urgent problems nowadays. The task is set to extend the effective operation of wells, optimize production management at the late stage of field development. Based on the task set, the article provides an overview of possible solutions in waterflooding management problems. Production management technology is considered as an alternative to intensification of operation, which is associated with an increase in the produciton rate and involves finding solutions aimed at reducing the water cut of well production. The practical implementation of the “Neural technologies for production improvement” includes the following steps: evaluation, selection, predictive analytics. The result is a digital technological regime of wells that corresponds to the set goal and the solution of the optimization problem in artificial intelligence algorithms using the software and hardware complex “Atlas – Waterflood Management”.“Neural technologies for production improvement” have been successfully tested at the pilot project site of the productive formation of the Vatyeganskoe field. The article provides a thorough and detailed analysis of the work performed, describes the algorithms and calculation results of the proxy model using the example of the pilot area, as well as the integration of the “Atlas – Waterflood Management” and the organization of the workflow with the field professionals of the Territorial Production Enterprise Povkhneftegaz.

Getting around in modern cities has become a daily puzzle for both residents and travelers. As cities increasingly evolve into complex hubs of innovation and development, the demand for efficient transportation solutions has never been higher. In the multifaceted field of urban transportation, cost-effective and efficient mobility remains a high priority. This paper looks at how cities are planning better ways for people to travel and move within the growing scope of multimodal transportation, a paradigm shift beyond traditional single-mode transit systems. Our approach to improving urban transport revolves around a few key principles: integration, innovation, and collaboration. Integration means bringing together different modes of transportation – like buses, trains, bikes, and even new technologies like ride-sharing services to make it easier for people to switch between them. This transformative approach not only aims to reduce congestion and reduce environmental footprints but also prioritizes user experience while ensuring a harmonious blend of convenience, sustainability, and accessibility. We use new technology and ideas to ensure that travel is easy, quick, and good for the environment. Looking at new trends and examples from around the world, this overview shows how cities are shaping a better future for everyone's daily commute. In this paper, we use Lingo software to solve a numerical example related to multi-modal transportation, demonstrating practical solutions for real-world implementation. Through innovation, we can find creative solutions to urban transportation challenges. Additionally, public transportation enhancements, such as the introduction of synchronized bus routes and electric vehicle charging stations, underline the commitment to sustainability and inclusivity. In the dynamic urban transportation landscape, cities will revolutionize our approach to providing cost-effective and efficient mobility solutions.

The non-spherical 6R manipulators are widely used in many fields. However, the non-spherical structure often poses challenges in the inverse kinematics problem (IKP) for such robots. To address this challenge, transforming IKP into an optimization problem is a promising solution. Nevertheless, existing optimization methods often entail complex computations and tend to overlook the geometric characteristics of the manipulators. In this study, we introduce a novel objective function based on a disconnect-reconnect method. Initially, based on the prior geometric knowledge of the non-spherical 6 degrees of freedom (DOF) manipulators, we employ a disconnect–reconnect strategy to decouple the kinematic equations. This process yields four nonlinear re-connection conditions equations. Subsequently, we utilize this equation to formulate a novel objective function. Then, we employ the adaptive covariance matrix evolution strategy (CMA-ES) alongside an analytical method to achieve precise solutions for the IKP. The proposed method was validated on the Comau NJ-220 manipulator. The simulation results demonstrate that the proposed effectively reduces computational complexity and enhances solution efficiency while maintaining accuracy in solving. [ABSTRACT FROM AUTHOR]

The paper is devoted to the problem of identification of hardening parameters of the developed two-level statistical model of polycrystal inelastic deformation taking into account dynamic recrystallization. First, the approximate values of the hardening parameters are obtained, and then they are refined as a result of formulating and solving the optimization problem using the Nelder–Mead method. The result is the set of the parameters that provides high accuracy of the empirical and calculated data on the stress-strain curves during inelastic deformation of a copper polycrystal. [ABSTRACT FROM AUTHOR]

Filter pruning has drawn much attention for releasing the limitations of person re-identification (ReID) in resource-constrained platforms due to the large parameters and high computational consumption. Filter pruning is depended on the given combination of pruning ratios to trim off unimportant filters, therefore, we define filter pruning as optimization problem. Existing strategies generally retrain all the pruning results and compare their performance iteratively, representing a major expenditure of time and effort. In this paper, we formally put forward a scheme of filter pruning based on evolutionary algorithms (EAFPruner) for ReID task for the first time, which automatically finds the optimal pruning structure in the pruning space with the better trade-off between computational cost and identification accuracy. Specifically, multiple evolutionary algorithms are adopted to solve the optimization problem by using mean average precision (mAP) as the fitness for filter-level sparsity. In addition, the adaptive batch normalization strategy is adopted to evaluate the pruned candidates generated by evolutionary algorithm without fine-tuning them to speed up training process. The experimental results are carried out on four person ReID datasets, and the number of parameters required by ResNet50 architecture is reduced by half while maintaining a comparable Rank-1 accuracy, which demonstrates the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. An improved Fireworks Algorithm (FWA) with elitism-based selection and optimal particle guidance strategies (EO-FWA) was proposed to address the limitations of the traditional FWA in terms of optimization accuracy and convergence speed, which not only improves the efficiency of the searching agent but also accelerates its convergence speed. In addition, by adopting boundary-based mapping rules, EO-FWA eliminates the randomness of traditional modulo operation mapping rules, which improves its stability and reliability. Twelve benchmark functions in CEC-BC-2022 are used to test the performance of EO-FWA, and the welded beam design problem is optimized at the end. The results show that EO-FWA exhibits stronger competitiveness than other algorithms in dealing with high-dimensional optimization problems and engineering optimization problem, and it can balance exploitation and exploration effectively so as to prevent the algorithm from falling into local optimal solutions. [ABSTRACT FROM AUTHOR]

Stock portfolio problems are one of the most relevant real-world problems. In this study, we discuss the portfolio's risk amount, rate of risk-return, and expected return rate under a Fermatean fuzzy environment. A linear programming problem is used to formulate a Fermatean fuzzy portfolio. The Fermatean fuzzy portfolio is converted to a deterministic form using the score function. Lingo software is used to solve these deterministic portfolio problems. The main feature of this model is that investors can select a risk coefficient to enhance predicted returns and customize their strategies according to their circumstances. An example is offered that illustrates the effectiveness and dependability of the proposed approach. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of the golden jackal optimization (GJO) in solving complex optimization problems, such as slow convergence speed and being easy to fall into local optimum, a hybrid-strategy improved golden jackal optimization (IGJO) is proposed. Firstly, when the optimal solution of the algorithm stagnates updating, the Cauchy variation strategy is introduced to enhance the population diversity and improve the global search capability of the algorithm to avoid falling into local optimum. Then, a decision strategy based on weight is proposed to accelerate the convergence of the algorithm by assigning different weights to golden jackal individuals. Experiments with eight benchmark functions and some CEC2017 test functions show that the improved algorithm has better optimization performance and convergence speed. Furthermore, the improved algorithm is applied to optimize the parameters of support vector regression (SVR) model, and its effectiveness is verified by experiments on 5 UCI (University of California, Irvine) datasets. [ABSTRACT FROM AUTHOR]

The development and implementation of various marketing strategies is one of the most vital uses of cost management system in businesses. The proposed study discusses the cost planning and management for a software system and thereby the emphasis is on software development cost. The ongoing upsurge of the IT industry has brought to light long-standing issues with software development, including unmanageable costs, missed deadlines, and variable quality. For such problems, we have developed a cost model and considered different cost components which impact the entire budget, the length of the testing period, and ultimately the quality of the software project being developed. Often, projects are delayed because of poor planning, an inefficient testing team, lack of software testing expertise, etc. As a result, we examine the cost of delivery time slippage as well as how it impacts the total budget and timeline for the intended product. With the aid of the software reliability growth model, we formulate a cost model for software development. Using this model, a project delivery time problem is being analyzed. For this we develop an optimization problem, based on cost-reliability criteria, i.e., we focus on minimizing overall estimated project costs while maintaining project reliability at or above a predefined level of aspiration. [ABSTRACT FROM AUTHOR]

The article proposes a parallel computing oriented method for solving the global minimum finding problems, in which continuous objective functions satisfy the Hölder condition, and the control parameters domain limited by continuous functions is characterized by a positive Lebesgue measure. A typical example of such a task is the discrepancy minimizing problem between the left and right parts of some large system of equilibrium equations (this is a usual situation when describing real process using Markov chain). The method is based on simple statistical tests, thanks to which, at each iteration, growing sets of potential global minima and sets of decrements necessary for estimating the values of the Hölder constants are formed. The article theoretically substantiates and empirically proves the guaranteed convergence of the authors’ method to the real global minimum, which occurs at an exponential rate. For the continuous iterations number, analytical upper estimates of the spacing between the potential global minima and real global minima are formalized, as well as an estimate of the probability of overcoming this spacing is formalized. The decrements sequence approximation, estimation of a priori unknown Hölder constants, estimation of the average number of iterations of the method and probabilistic characteristics of the final solution are analytically justified. In addition to the theoretical proof, the adequacy of the authors’ method has been confirmed empirically. It turned out that both the quality characteristics of the initial results calculated by the authors’ method and the time to obtain them are practically independent of the size of the search area. This expected result is a significant advantage of the authors’ method over analogues.