Journal Description
Mathematics
Mathematics
is a peer-reviewed, open access journal which provides an advanced forum for studies related to mathematics, and is published semimonthly online by MDPI. The European Society for Fuzzy Logic and Technology (EUSFLAT) and International Society for the Study of Information (IS4SI) are affiliated with Mathematics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), RePEc, and other databases.
- Journal Rank: JCR - Q1 (Mathematics) / CiteScore - Q1 (General Mathematics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 13 topical sections.
- Companion journals for Mathematics include: Foundations, AppliedMath, Analytics, International Journal of Topology, Geometry and Logics.
Impact Factor:
2.4 (2022);
5-Year Impact Factor:
2.3 (2022)
Latest Articles
The Adjoint of α-Times-Integrated C-Regularized Semigroups
Mathematics 2024, 12(10), 1561; https://doi.org/10.3390/math12101561 (registering DOI) - 16 May 2024
Abstract
We consider an operator on a Banach space X with generator A, characterized by being an -times-integrated C-regularized semigroup. The adjoint family
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We consider an operator on a Banach space X with generator A, characterized by being an -times-integrated C-regularized semigroup. The adjoint family is introduced for analysis. maintains the characteristics of an -times-integrated C-regularized semigroup, though with strong continuity and Bochner integrals being substituted by weak continuity and weak integrals, respectively. Our investigation focuses on the closed subspace , where exhibits strong continuity. Additionally, a comparison between the adjoint of A and the generator of the adjoint family is conducted.
Full article
(This article belongs to the Special Issue Functional Differential Equations: Theory and Applications, 2nd Edition)
Open AccessArticle
Interior Design Evaluation Based on Deep Learning: A Multi-Modal Fusion Evaluation Mechanism
by
Yiyan Fan, Yang Zhou and Zheng Yuan
Mathematics 2024, 12(10), 1560; https://doi.org/10.3390/math12101560 - 16 May 2024
Abstract
The design of 3D scenes is of great significance, and one of the crucial areas is interior scene design. This study not only pertains to the living environment of individuals but also has applications in the design and development of virtual environments. Previous
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The design of 3D scenes is of great significance, and one of the crucial areas is interior scene design. This study not only pertains to the living environment of individuals but also has applications in the design and development of virtual environments. Previous work on indoor scenes has focused on understanding and editing existing indoor scenes, such as scene reconstruction, segmentation tasks, texture, object localization, and rendering. In this study, we propose a novel task in the realm of indoor scene comprehension, amalgamating interior design principles with professional evaluation criteria: 3D indoor scene design assessment. Furthermore, we propose an approach using a transformer encoder–decoder architecture and a dual-graph convolutional network. Our approach facilitates users in posing text-based inquiries; accepts input in two modalities, point cloud representations of indoor scenes and textual queries; and ultimately generates a probability distribution indicating positive, neutral, and negative assessments of interior design. The proposed method uses separately pre-trained modules, including a 3D visual question-answering module and a dual-graph convolutional network for identifying emotional tendencies of text.
Full article
(This article belongs to the Special Issue Advances of Intelligent Systems)
Open AccessArticle
Some Oscillatory Criteria for Second-Order Emden–Fowler Neutral Delay Differential Equations
by
Haifeng Tian and Rongrong Guo
Mathematics 2024, 12(10), 1559; https://doi.org/10.3390/math12101559 - 16 May 2024
Abstract
In this paper, by using the Riccati transformation and integral inequality technique, we establish several oscillation criteria for second-order Emden–Fowler neutral delay differential equations under the canonical case and non-canonical case, respectively. Compared with some recent results reported in the literature, we extend
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In this paper, by using the Riccati transformation and integral inequality technique, we establish several oscillation criteria for second-order Emden–Fowler neutral delay differential equations under the canonical case and non-canonical case, respectively. Compared with some recent results reported in the literature, we extend the range of the neutral coefficient. Therefore, our results generalize to some of the results presented in the literature. Furthermore, several examples are provided to illustrate our conclusions.
Full article
(This article belongs to the Special Issue Mathematical Modeling and Simulation of Oscillatory Phenomena, 2nd Edition)
Open AccessArticle
Linear Generalized n-Derivations on C-Algebras*
by
Shakir Ali, Amal S. Alali and Vaishali Varshney
Mathematics 2024, 12(10), 1558; https://doi.org/10.3390/math12101558 - 16 May 2024
Abstract
Let be a fixed integer and be a -algebra. A permuting n-linear map is known to be symmetric generalized n-derivation if there exists a symmetric n-derivation
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Let be a fixed integer and be a -algebra. A permuting n-linear map is known to be symmetric generalized n-derivation if there exists a symmetric n-derivation such that holds ∀ . In this paper, we investigate the structure of -algebras involving generalized linear n-derivations. Moreover, we describe the forms of traces of linear n-derivations satisfying certain functional identity.
Full article
(This article belongs to the Special Issue New Advances in Algebra, Ring Theory and Homological Algebra, 2nd Edition)
Open AccessArticle
A Revisit to Sunk Cost Fallacy for Two-Stage Stochastic Binary Decision Making
by
Xuecheng Tian, Bo Jiang, King-Wah Pang, Yuquan Du, Yong Jin and Shuaian Wang
Mathematics 2024, 12(10), 1557; https://doi.org/10.3390/math12101557 - 16 May 2024
Abstract
This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are
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This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making.
Full article
(This article belongs to the Special Issue Mathematical Optimization and Decision Making Analysis)
Open AccessArticle
Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain
by
Juraj Bodik
Mathematics 2024, 12(10), 1556; https://doi.org/10.3390/math12101556 - 16 May 2024
Abstract
The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose–response function (also called the effect curve) is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work
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The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose–response function (also called the effect curve) is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potentially beyond the range of observed values—that is, estimating for very large t. Our approach is grounded in the field of statistics known as extreme value theory. We outline key assumptions for the identifiability of the extreme treatment effect. Additionally, we present a novel and consistent estimation procedure that can potentially reduce the dimension of the confounders to at most 3. This is a significant result since typically, the estimation of is very challenging due to high-dimensional confounders. In practical applications, our framework proves valuable when assessing the effects of scenarios such as drug overdoses, extreme river discharges, or extremely high temperatures on a variable of interest.
Full article
(This article belongs to the Special Issue Computational Statistical Methods and Extreme Value Theory)
Open AccessArticle
Analysis of Heat Transfer for the Copper–Water Nanofluid Flow through a Uniform Porous Medium Generated by a Rotating Rigid Disk
by
Naif Abdulaziz M. Alkuhayli and Andrew Morozov
Mathematics 2024, 12(10), 1555; https://doi.org/10.3390/math12101555 - 16 May 2024
Abstract
This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated);
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This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated); therefore, there are velocity and temperature slips between the nanofluid and the disk surface. The importance of considering slip conditions in modeling nanofluids comes from practical applications where rotating parts of machines may be coated. Additionally, this study examines the influence of heat generation on the temperature distribution within the flow. By transforming the original Navier–Stokes partial differential equations (PDEs) into a system of ordinary differential equations (ODEs), numerical solutions are obtained. The boundary conditions for velocity and temperature slips are formulated using the effective viscosity and thermal conductivity of the copper–water nanofluid. The dependence of the velocity and temperature fields in the nanofluid flow on key parameters is investigated. The major findings of the study are that the nanoparticle volume fraction significantly impacts the temperature distribution, particularly in the presence of a heat source. Furthermore, polishing the disk surface enhances velocity slips, reducing stresses at the disk surface, while a pronounced velocity slip leads to distinct changes in the radial, azimuthal, and axial velocity components. The study highlights the influence of slip conditions on fluid velocity as compared to previously considered non-slip conditions. This suggests that accounting for slip conditions for coated rotating disks would yield more accurate predictions in assessing heat transfer, which would be potentially important for the practical design of various devices using nanofluids.
Full article
(This article belongs to the Special Issue Numerical Analysis and Scientific Computing in Applied Mathematics)
Open AccessArticle
Constrained Symmetric Non-Negative Matrix Factorization with Deep Autoencoders for Community Detection
by
Wei Zhang, Shanshan Yu, Ling Wang, Wei Guo and Man-Fai Leung
Mathematics 2024, 12(10), 1554; https://doi.org/10.3390/math12101554 - 16 May 2024
Abstract
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Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces
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Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model’s capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks.
Full article
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Open AccessArticle
Feedback Stabilization of Quasi-One-Sided Lipschitz Nonlinear Discrete-Time Systems with Reduced-Order Observer
by
Yanbin Zhao and Wenqiang Dong
Mathematics 2024, 12(10), 1553; https://doi.org/10.3390/math12101553 - 16 May 2024
Abstract
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The feedback stabilization problem for nonlinear discrete-time systems with a reduced-order observer is investigated, in which the nonlinear terms of the systems satisfy the quasi-one-sided Lipschitz condition. First, a discrete-time reduced-order observer for nonlinear systems is designed. Then, a feedback controller with a
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The feedback stabilization problem for nonlinear discrete-time systems with a reduced-order observer is investigated, in which the nonlinear terms of the systems satisfy the quasi-one-sided Lipschitz condition. First, a discrete-time reduced-order observer for nonlinear systems is designed. Then, a feedback controller with a reduced-order observer is designed for realizing the stabilization of nonlinear discrete-time systems. We prove that the design of a feedback controller and reduced-order observer of systems can be carried out independently in the case of discrete-time with nonlinear terms, which largely reduces the computational complexity of the observer and controller. The introduction of the quasi-one-sided Lipschitz condition simultaneously enhances the robustness and stability of nonlinear control systems. Finally, the feasibility and effectiveness of the proposed design approach is verified by a numerical simulation.
Full article
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Open AccessArticle
New Uses of q-Generalized Janowski Function in q-Bounded Turning Functions
by
Timilehin Gideon Shaba, Ferdous M. O. Tawfiq, Daniel Breaz and Luminit̨a-Ioana Cotiîrlă
Mathematics 2024, 12(10), 1552; https://doi.org/10.3390/math12101552 (registering DOI) - 16 May 2024
Abstract
In this paper, we discussed a new subclass of bi-univalent functions in the unit disk using q-generalized Janowski function and q-derivative. Additionally, certain properties were examined and effectively demonstrated, such as
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In this paper, we discussed a new subclass of bi-univalent functions in the unit disk using q-generalized Janowski function and q-derivative. Additionally, certain properties were examined and effectively demonstrated, such as the second Hankel determinant, Fekete–Szegö estimates, and Coefficients Bounds. Each of these bounds were precise and were confirmed by finding the extremal function for the new class. Furthermore, there are in-depth conversations available regarding certain intriguing specific cases of the outcomes achieved.
Full article
Open AccessArticle
AMSMC-UGAN: Adaptive Multi-Scale Multi-Color Space Underwater Image Enhancement with GAN-Physics Fusion
by
Dong Chao, Zhenming Li, Wenbo Zhu, Haibing Li, Bing Zheng, Zhongbo Zhang and Weijie Fu
Mathematics 2024, 12(10), 1551; https://doi.org/10.3390/math12101551 - 16 May 2024
Abstract
Underwater vision technology is crucial for marine exploration, aquaculture, and environmental monitoring. However, the challenging underwater conditions, including light attenuation, color distortion, reduced contrast, and blurring, pose difficulties. Current deep learning models and traditional image enhancement techniques are limited in addressing these challenges,
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Underwater vision technology is crucial for marine exploration, aquaculture, and environmental monitoring. However, the challenging underwater conditions, including light attenuation, color distortion, reduced contrast, and blurring, pose difficulties. Current deep learning models and traditional image enhancement techniques are limited in addressing these challenges, making it challenging to acquire high-quality underwater image signals. To overcome these limitations, this study proposes an approach called adaptive multi-scale multi-color space underwater image enhancement with GAN-physics fusion (AMSMC-UGAN). AMSMC-UGAN leverages multiple color spaces (RGB, HSV, and Lab) for feature extraction, compensating for RGB’s limitations in underwater environments and enhancing the use of image information. By integrating a membership degree function to guide deep learning based on physical models, the model’s performance is improved across different underwater scenes. In addition, the introduction of a multi-scale feature extraction module deepens the granularity of image information, learns the degradation distribution of different image information of the same image content more comprehensively, and provides useful guidance for more comprehensive data for image enhancement. AMSMC-UGAN achieved maximum scores of 26.04 dB, 0.87, and 3.2004 for PSNR, SSIM, and UIQM metrics, respectively, on real and synthetic underwater image datasets. Additionally, it obtained gains of at least 6.5%, 6%, and 1% for these metrics. Empirical evaluations on real and artificially distorted underwater image datasets demonstrate that AMSMC-GAN outperforms existing techniques, showcasing superior performance with enhanced quantitative metrics and strong generalization capabilities.
Full article
(This article belongs to the Special Issue Mathematics for Visual Computing: Acquisition, Processing, Analysis and Rendering of Visual Information)
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Open AccessArticle
Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations
by
Jan Weymeirsch, Julian Ernst and Ralf Münnich
Mathematics 2024, 12(10), 1550; https://doi.org/10.3390/math12101550 - 16 May 2024
Abstract
Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data.
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Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data. However, estimating such models on the regional level is often not feasible due to limited sample size and lack of geographic information. Simply applying the model estimated at the national level to all geographies leads to biased state transitions due to regional differences in level and distribution. In this paper, we introduce a model-based alignment method to adapt predicted probabilities obtained from a nationally estimated model to subregions by integrating known marginal distributions to re-introduce regional heterogeneity and create more realistic trajectories, particularly in small areas. We show that the model-adjusted transition probabilities can capture region-specific patterns and lead to improved projections. Our findings are useful to researchers who want to harmonise model outputs with external information, in particular for the field of microsimulation.
Full article
(This article belongs to the Special Issue Applied Stochastic Solutions, Dynamic Analysis, and Mathematical Models for Issues in Demography, Epidemiology, and Environmetrics)
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Open AccessArticle
Music Genre Classification Based on VMD-IWOA-XGBOOST
by
Rumeijiang Gan, Tichen Huang, Jin Shao and Fuyu Wang
Mathematics 2024, 12(10), 1549; https://doi.org/10.3390/math12101549 - 15 May 2024
Abstract
Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features
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Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria.
Full article
Open AccessArticle
Beyond Event-Centric Narratives: Advancing Arabic Story Generation with Large Language Models and Beam Search
by
Arwa Alhussain and Aqil M. Azmi
Mathematics 2024, 12(10), 1548; https://doi.org/10.3390/math12101548 - 15 May 2024
Abstract
In the domain of automated story generation, the intricacies of the Arabic language pose distinct challenges. This study introduces a novel methodology that moves away from conventional event-driven narrative frameworks, emphasizing the restructuring of narrative constructs through sophisticated language models. Utilizing mBERT, our
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In the domain of automated story generation, the intricacies of the Arabic language pose distinct challenges. This study introduces a novel methodology that moves away from conventional event-driven narrative frameworks, emphasizing the restructuring of narrative constructs through sophisticated language models. Utilizing mBERT, our approach begins by extracting key story entities. Subsequently, XLM-RoBERTa and a BERT-based linguistic evaluation model are employed to direct beam search algorithms in the replacement of these entities. Further refinement is achieved through Low-Rank Adaptation (LoRA), which fine-tunes the extensive 3 billion-parameter BLOOMZ model specifically for generating Arabic narratives. Our methodology underwent thorough testing and validation, involving individual assessments of each submodel. The ROCStories dataset provided the training ground for our story entity extractor and new entity generator, and was also used in the fine-tuning of the BLOOMZ model. Additionally, the Arabic ComVE dataset was employed to train our commonsense evaluation model. Our extensive analyses yield crucial insights into the efficacy of our approach. The story entity extractor demonstrated robust performance with an F-score of 96.62%. Our commonsense evaluator reported an accuracy of 84.3%, surpassing the previous best by 3.1%. The innovative beam search strategy effectively produced entities that were linguistically and semantically superior to those generated using baseline models. Further subjective evaluations affirm our methodology’s capability to generate high-quality Arabic stories characterized by linguistic fluency and logical coherence.
Full article
(This article belongs to the Section Mathematics and Computer Science)
Open AccessArticle
Key Selection Factors Influencing Animation Films from the Perspective of the Audience
by
Wendong Jiang
Mathematics 2024, 12(10), 1547; https://doi.org/10.3390/math12101547 - 15 May 2024
Abstract
The animation industry is an important part of China’s cultural and creative industries. In fact, it is the leading cultural and creative industry in China. However, there is insufficient research on the audience’s views in China’s animation industry, which has become an important
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The animation industry is an important part of China’s cultural and creative industries. In fact, it is the leading cultural and creative industry in China. However, there is insufficient research on the audience’s views in China’s animation industry, which has become an important research gap. Thus, an integrated approach of FAHP and GRA is proposed in this study, to analyse and evaluate the key selection factors for the Chinese animation industry from the perspective of a Chinese audience. In this research, in both FAHP and GRA models, factors such as visual appealing character, interesting performance of character animation, and easy-to-understand storyline are prioritised conditions for the selection of Chinese animation from the perspective of Chinese audiences. The main contribution of this research is to underscore the value of the hybrid MCDM model to aid Chinese animation companies in aligning their productions with audience expectations and making informed decisions. Finally, this study offers a systematic and objective model for Chinese animation selection, providing practical insights that can be applied in the industry and can serve as a valuable reference for future research in similar domains.
Full article
(This article belongs to the Special Issue Selected Papers from the International Conference of Numerical Analysis and Applied Mathematics (ICNAAM))
Open AccessArticle
A Dynamic Programming Approach to the Collision Avoidance of Autonomous Ships
by
Raphael Zaccone
Mathematics 2024, 12(10), 1546; https://doi.org/10.3390/math12101546 - 15 May 2024
Abstract
The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among
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The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, dynamic programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for the collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. We put the proposed algorithms to the test in realistic navigation scenarios and also develop an extensive test on a large set of randomly generated scenarios, comparing them with the RRT* algorithm using performance metrics proposed in the literature. The results show the potential benefits of an autonomous navigation or decision support framework.
Full article
(This article belongs to the Special Issue Dynamic Programming)
Open AccessArticle
On Some Multipliers Related to Discrete Fractional Integrals
by
Jinhua Cheng
Mathematics 2024, 12(10), 1545; https://doi.org/10.3390/math12101545 - 15 May 2024
Abstract
This paper explores the properties of multipliers associated with discrete analogues of fractional integrals, revealing intriguing connections with Dirichlet characters, Euler’s identity, and Dedekind zeta functions of quadratic imaginary fields. Employing Fourier transform techniques, the Hardy–Littlewood circle method, and a discrete analogue of
[...] Read more.
This paper explores the properties of multipliers associated with discrete analogues of fractional integrals, revealing intriguing connections with Dirichlet characters, Euler’s identity, and Dedekind zeta functions of quadratic imaginary fields. Employing Fourier transform techniques, the Hardy–Littlewood circle method, and a discrete analogue of the Stein–Weiss inequality on product space through implication methods, we establish bounds for these operators. Our results contribute to a deeper understanding of the intricate relationship between number theory and harmonic analysis in discrete domains, offering insights into the convergence behavior of these operators.
Full article
(This article belongs to the Special Issue Fractional Calculus and Mathematical Applications, 2nd Edition)
Open AccessArticle
Blockchain-Based Unbalanced PSI with Public Verification and Financial Security
by
Zhanshan Wang and Xiaofeng Ma
Mathematics 2024, 12(10), 1544; https://doi.org/10.3390/math12101544 - 15 May 2024
Abstract
Private set intersection (PSI) enables two parties to determine the intersection of their respective datasets without revealing any information beyond the intersection itself. This paper particularly focuses on the scenario of unbalanced PSI, where the sizes of datasets possessed by the parties can
[...] Read more.
Private set intersection (PSI) enables two parties to determine the intersection of their respective datasets without revealing any information beyond the intersection itself. This paper particularly focuses on the scenario of unbalanced PSI, where the sizes of datasets possessed by the parties can significantly differ. Current protocols for unbalanced PSI under the malicious security model exhibit low efficiency, rendering them impractical in real-world applications. By contrast, most efficient unbalanced PSI protocols fail to guarantee the correctness of the intersection against a malicious server and cannot even ensure the client’s privacy. The present study proposes a blockchain-based unbalanced PSI protocol with public verification and financial security that enables the client to detect malicious behavior from the server (if any) and then generate an irrefutable and publicly verifiable proof without compromising its secret. The proof can be verified through smart contracts, and some economic incentive and penalty measures are executed automatically to achieve financial security. Furthermore, we implement the proposed protocol, and experimental results demonstrate that our scheme exhibits low online communication complexity and computational overhead for the client. At the same time, the size of the generated proof and its verification complexity are both , enabling cost-effective validation on the blockchain.
Full article
(This article belongs to the Special Issue Applied Mathematics in Blockchain and Intelligent Systems)
Open AccessArticle
A Fluid Dynamic Approach to Model and Optimize Energy Flows in Networked Systems
by
Massimo de Falco, Luigi Rarità and Alfredo Vaccaro
Mathematics 2024, 12(10), 1543; https://doi.org/10.3390/math12101543 - 15 May 2024
Abstract
In this paper, attention is focused on the analysis and optimization of energy flows in networked systems via a fluid-dynamic approach. Considering the real case of an energy hub, the proposed model deals with conservation laws on arcs and linear programming problems at
[...] Read more.
In this paper, attention is focused on the analysis and optimization of energy flows in networked systems via a fluid-dynamic approach. Considering the real case of an energy hub, the proposed model deals with conservation laws on arcs and linear programming problems at nodes. Optimization of the energy flows is accomplished by considering a cost functional, which estimates a term proportional to the kinetic energy of the overall system in consideration. As the real optimization issue deals with an integral formulation for which precise solutions have to be studied through variational methods, a decentralized approach is considered. First, the functional is optimized for a simple network having a unique node, with an incoming arc and two outgoing ones. The optimization deals with distribution coefficients, and explicit solutions are found. Then, global optimization is obtained via the local optimal parameters at the various nodes of the real system. The obtained results prove the correctness of the proposed approach and show the evident advantages of optimization procedures dealing with variational approaches.
Full article
(This article belongs to the Topic Mathematical Modeling)
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Deep Learning-Driven Interference Perceptual Multi-Modulation for Full-Duplex Systems
by
Taehyoung Kim and Gyuyeol Kong
Mathematics 2024, 12(10), 1542; https://doi.org/10.3390/math12101542 - 15 May 2024
Abstract
In this paper, a novel data transmission scheme, interference perceptual multi-modulation (IP-MM), is proposed for full-duplex (FD) systems. In order to unlink the conventional uplink (UL) data transmission using a single modulation and coding scheme (MCS) over the entire assigned UL bandwidth, IP-MM
[...] Read more.
In this paper, a novel data transmission scheme, interference perceptual multi-modulation (IP-MM), is proposed for full-duplex (FD) systems. In order to unlink the conventional uplink (UL) data transmission using a single modulation and coding scheme (MCS) over the entire assigned UL bandwidth, IP-MM enables the transmission of UL data channels based on multiple MCS levels, where a different MCS level is applied to each subband of UL transmission. In IP-MM, a deep convolutional neural network is used for MCS-level prediction for each UL subband by estimating the potential residual self-interference (SI) according to the downlink (DL) resource allocation pattern. In addition, a subband-based UL transmission procedure is introduced from a specification point of view to enable IP-MM-based UL transmission. The benefits of IP-MM are verified using simulations, and it is observed that IP-MM achieves approximately 20% throughput gain compared to the conventional UL transmission scheme.
Full article
(This article belongs to the Topic Application of Deep Learning Method in 6G Communication Technology)
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Mathematics
New Trends on Boundary Value Problems
Guest Editors: Miklós Rontó, András Rontó, Nino Partsvania, Bedřich Půža, Hriczó KrisztiánDeadline: 31 May 2024
Special Issue in
Mathematics
Applications of Fuzzy Modeling in Risk Management
Guest Editors: Edit Toth-Laufer, László PokorádiDeadline: 20 June 2024
Special Issue in
Mathematics
Computational Statistical Methods and Extreme Value Theory
Guest Editor: Frederico CaeiroDeadline: 30 June 2024
Topical Collections
Topical Collection in
Mathematics
Topology and Foundations
Collection Editors: Lorentz Jäntschi, Dušanka Janežič
Topical Collection in
Mathematics
Multiscale Computation and Machine Learning
Collection Editors: Yalchin Efendiev, Eric Chung
Topical Collection in
Mathematics
Theoretical and Mathematical Ecology
Collection Editor: Yuri V. Tyutyunov