Mathematics

21 pages, 5960 KiB

Open AccessArticle

Effective SQL Injection Detection: A Fusion of Binary Olympiad Optimizer and Classification Algorithm

by Bahman Arasteh, Asgarali Bouyer, Seyed Salar Sefati and Razvan Craciunescu

Mathematics 2024, 12(18), 2917; https://doi.org/10.3390/math12182917 (registering DOI) - 19 Sep 2024

Since SQL injection allows attackers to interact with the database of applications, it is regarded as a significant security problem. By applying machine learning algorithms, SQL injection attacks can be identified. Problem: In the training stage of machine learning methods, effective features [...] Read more.

Since SQL injection allows attackers to interact with the database of applications, it is regarded as a significant security problem. By applying machine learning algorithms, SQL injection attacks can be identified. Problem: In the training stage of machine learning methods, effective features are used to develop an optimal classifier that is highly accurate. The specification of the features with the highest efficacy is considered to be an NP-complete combinatorial optimization challenge. Selecting the most effective features refers to the procedure of identifying the smallest and most effective features in the dataset. The rationale behind this paper is to optimize the accuracy, precision, and sensitivity parameters of the SQL injection attack detection method. Method: In this paper, a method for identifying SQL injection attacks was suggested. In the first step, a particular training dataset that included 13 features was developed. In the second step, to specify the best features of the dataset, a specific binary variety of the Olympiad optimization algorithm was developed. Various machine learning algorithms were used to create the optimal attack detector. Results: Based on the experiments carried out, the suggested SQL injection detector using an artificial neural network and the feature selector can achieve 99.35% accuracy, 100% precision, and 100% sensitivity. Owing to selecting about 30% of the effective features, the proposed method enhanced the efficacy of SQL injection detectors. Full article

► Show Figures

Figure 1

20 pages, 795 KiB

Open AccessArticle

Reliability Modeling of Systems with Undetected Degradation Considering Time Delays, Self-Repair, and Random Operating Environments

by Hoang Pham

Mathematics 2024, 12(18), 2916; https://doi.org/10.3390/math12182916 (registering DOI) - 19 Sep 2024

Abstract

In some settings, systems may not fail completely but instead undergo performance degradation, leading to reduced efficiency. A significant concern arises when a system transitions into a degraded state without immediate detection, with the degradation only becoming apparent after an unpredictable period. Undetected [...] Read more.

In some settings, systems may not fail completely but instead undergo performance degradation, leading to reduced efficiency. A significant concern arises when a system transitions into a degraded state without immediate detection, with the degradation only becoming apparent after an unpredictable period. Undetected degradation can result in failures with significant consequences. For instance, a minor crack in an oil pipeline might go unnoticed, eventually leading to a major leak, environmental harm, and costly cleanup efforts. Similarly, in the nuclear industry, undetected degradation in reactor cooling systems could cause overheating and potentially catastrophic failure. This paper focuses on reliability modeling for systems experiencing degradation, accounting for time delays associated with undetected degraded states, self-repair mechanisms, and varying operating environments. The paper presents a reliability model for degraded, time-dependent systems, incorporating various aspects of degradation. It first discusses the model assumptions and formulation, followed by numerical results obtained from system modeling using the developed program. Various scenarios are illustrated, incorporating time delays and different parameter values. Through computational analysis of these complex systems, we observe that the probability of the system being in the undetected degraded state tends to stabilize shortly after the initial degradation begins. The model is valuable for predicting and establishing an upper bound on the probability of the undetected, degraded state and the system’s overall reliability. Finally, the paper outlines potential avenues for future research. Full article

(This article belongs to the Special Issue Application of Mathematical Methods in Industrial Engineering and Management: 2nd Edition)

17 pages, 306 KiB

Open AccessArticle

On LP-Kenmotsu Manifold with Regard to Generalized Symmetric Metric Connection of Type (α, β)

by Doddabhadrappla Gowda Prakasha, Nasser Bin Turki, Mathad Veerabhadraswamy Deepika and İnan Ünal

Mathematics 2024, 12(18), 2915; https://doi.org/10.3390/math12182915 - 19 Sep 2024

Abstract

In the current article, we examine Lorentzian para-Kenmotsu (shortly, LP-Kenmotsu) manifolds with regard to the generalized symmetric metric connection

\nabla^{G}

of type

(α, β)

. First, we obtain the expressions for curvature tensor, Ricci tensor and scalar curvature of [...] Read more.

In the current article, we examine Lorentzian para-Kenmotsu (shortly, LP-Kenmotsu) manifolds with regard to the generalized symmetric metric connection

\nabla^{G}

of type

(α, β)

. First, we obtain the expressions for curvature tensor, Ricci tensor and scalar curvature of an LP-Kenmotsu manifold with regard to the connection

\nabla^{G}

. Next, we analyze LP-Kenmotsu manifolds equipped with the connection

\nabla^{G}

that are locally symmetric, Ricci semi-symmetric, and

φ

-Ricci symmetric and also demonstrated that in all these situations the manifold is an Einstein one with regard to the connection

\nabla^{G}

. Moreover, we obtain some conclusions about projectively flat, projectively semi-symmetric and

φ

-projectively flat LP-Kenmotsu manifolds concerning the connection

\nabla^{G}

along with several consequences through corollaries. Ultimately, we provide a 5-dimensional LP-Kenmotsu manifold example to validate the derived expressions. Full article

(This article belongs to the Special Issue Differentiable Manifolds and Geometric Structures)

12 pages, 258 KiB

Open AccessArticle

Projective Vector Fields on Semi-Riemannian Manifolds

by Norah Alshehri and Mohammed Guediri

Mathematics 2024, 12(18), 2914; https://doi.org/10.3390/math12182914 - 19 Sep 2024

Abstract

This paper explores the properties of projective vector fields on semi-Riemannian manifolds. The main result establishes that if a projective vector field P on such a manifold is also a conformal vector field with potential function

ψ

and the vector field

ζ

dual [...] Read more.

This paper explores the properties of projective vector fields on semi-Riemannian manifolds. The main result establishes that if a projective vector field P on such a manifold is also a conformal vector field with potential function

ψ

and the vector field

ζ

dual to

d ψ

does not change its causal character, then P is homothetic, or

ζ

is a light-like vector field. Additionally, it is shown that a complete Riemannian manifold admits a projective vector field that is also conformal and non-Killing if and only if it is locally Euclidean. The paper also presents other results related to the characterization of Killing and parallel vector fields using the Ricci curvature and the Hessian of the function given by the inner product of the vector field. Full article

(This article belongs to the Special Issue Differentiable Manifolds and Geometric Structures)

15 pages, 285 KiB

Open AccessArticle

A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis

by Qiang Deng

Mathematics 2024, 12(18), 2913; https://doi.org/10.3390/math12182913 - 19 Sep 2024

Abstract

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the [...] Read more.

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts. Full article

21 pages, 2113 KiB

Open AccessArticle

Periodic Scheduling Optimization for Dual-Arm Cluster Tools with Arm Task and Residency Time Constraints via Petri Net Model

by Lei Gu, Naiqi Wu, Tan Li, Siwei Zhang and Wenyu Wu

Mathematics 2024, 12(18), 2912; https://doi.org/10.3390/math12182912 - 19 Sep 2024

Abstract

In order to improve quality assurance in wafer manufacturing, there are strict process requirements. Besides the well-known residency time constraints (RTCs), a dual-arm cluster tool also requires each robot arm to execute a specific set of tasks. We call such a tool an [...] Read more.

In order to improve quality assurance in wafer manufacturing, there are strict process requirements. Besides the well-known residency time constraints (RTCs), a dual-arm cluster tool also requires each robot arm to execute a specific set of tasks. We call such a tool an arm task-constrained dual-arm cluster tool (ATC-DACT). To do this, one of the arms is identified as the dirty one and the other as the clean one. The dirty one can deal with raw wafers, while the clean one can deal with processed wafers. This requirement raises a new problem for scheduling a cluster tool. This paper discusses the scheduling problem of ATC-DACTs with RTCs. Due to the arm task constraints, the proven, effective swap strategy is no longer applicable to ATC-DACTs, making the scheduling problem difficult. To address this problem, we explicitly describe the robot waiting as an event and build a Petri net (PN) model. Then, we propose a hybrid task sequence (HTS) as an operation strategy by combining the swap and backward strategies. Based on the HTS, the necessary and sufficient conditions for schedulability are established; also, a linear programming model is developed. We then develop an algorithm using these results to optimally schedule the system. Industrial case studies demonstrate the application of this method. Full article

(This article belongs to the Section Computational and Applied Mathematics)

► Show Figures

Figure 1

14 pages, 539 KiB

Open AccessArticle

Anti-Persistent Values of the Hurst Exponent Anticipate Mean Reversion in Pairs Trading: The Cryptocurrencies Market as a Case Study

by Mar Grande, Florentino Borondo, Juan Carlos Losada and Javier Borondo

Mathematics 2024, 12(18), 2911; https://doi.org/10.3390/math12182911 - 19 Sep 2024

Abstract

Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean [...] Read more.

Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include

H < 0.5

as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market. Full article

(This article belongs to the Special Issue Chaos Theory and Its Applications to Economic Dynamics)

► Show Figures

Figure 1

13 pages, 272 KiB

Open AccessArticle

Three Weak Solutions for a Critical Non-Local Problem with Strong Singularity in High Dimension

by Gabriel Neves Cunha, Francesca Faraci and Kaye Silva

Mathematics 2024, 12(18), 2910; https://doi.org/10.3390/math12182910 - 18 Sep 2024

Abstract

In this paper, we deal with a strongly singular problem involving a non-local operator, a critical nonlinearity, and a subcritical perturbation. We apply techniques from non-smooth analysis to the energy functional, in combination with the study of the topological properties of the sublevels [...] Read more.

In this paper, we deal with a strongly singular problem involving a non-local operator, a critical nonlinearity, and a subcritical perturbation. We apply techniques from non-smooth analysis to the energy functional, in combination with the study of the topological properties of the sublevels of its smooth part, to prove the existence of three weak solutions: two points of local minimum and a third one as a mountain pass critical point. Full article

(This article belongs to the Special Issue Problems and Methods in Nonlinear Analysis)

17 pages, 461 KiB

Open AccessArticle

LMKCDEY Revisited: Speeding Up Blind Rotation with Signed Evaluation Keys

by Yongwoo Lee

Mathematics 2024, 12(18), 2909; https://doi.org/10.3390/math12182909 - 18 Sep 2024

Abstract

Recently, Lee et al. introduced a novel blind rotation technique utilizing ring automorphisms also known as LMKCDEY. Among known prominent blind rotation methods, LMKCDEY stands out because of its minimal key size and efficient runtime for arbitrary secret keys, although Chillotti et al.’s [...] Read more.

Recently, Lee et al. introduced a novel blind rotation technique utilizing ring automorphisms also known as LMKCDEY. Among known prominent blind rotation methods, LMKCDEY stands out because of its minimal key size and efficient runtime for arbitrary secret keys, although Chillotti et al.’s approach, commonly referred to as CGGI, offers faster runtime when using binary or ternary secrets. In this paper, we propose an enhancement to LMKCDEY’s runtime by incorporating auxiliary keys that encrypt the negated values of secret key elements. Our method not only achieves faster execution than LMKCDEY but also maintains a smaller key size compared to the ternary version of CGGI. Moreover, the proposed technique is compatible with LMKCDEY with only minimal adjustments. Experimental results with OpenFHE demonstrate that our approach can improve bootstrapping runtime by 5–28%, depending on the chosen parameters. Full article

(This article belongs to the Special Issue New Advances in Applied Cryptography, Network Security and Data Privacy)

► Show Figures

Figure 1

17 pages, 690 KiB

Open AccessArticle

A Privacy-Preserving Electromagnetic-Spectrum-Sharing Trading Scheme Based on ABE and Blockchain

by Xing Pu, Ruixian Wang and Xin Lu

Mathematics 2024, 12(18), 2908; https://doi.org/10.3390/math12182908 - 18 Sep 2024

Abstract

The electromagnetic spectrum is a limited resource. With the widespread application of the electromagnetic spectrum in various fields, the contradiction between the demand for the electromagnetic spectrum and electromagnetic spectrum resources has become increasingly prominent. Spectrum sharing is an effective way to improve [...] Read more.

The electromagnetic spectrum is a limited resource. With the widespread application of the electromagnetic spectrum in various fields, the contradiction between the demand for the electromagnetic spectrum and electromagnetic spectrum resources has become increasingly prominent. Spectrum sharing is an effective way to improve the utilization of the electromagnetic spectrum. However, there are many challenges in existing distributed electromagnetic spectrum trading based on blockchain technology. Since a blockchain does not provide privacy protection, the risk of privacy leakage during the trading process makes electromagnetic spectrum owners unwilling to share. In addition, a blockchain only guarantees integrity, and the imperfect trading dispute resolution mechanism causes electromagnetic spectrum owners to be afraid to share. Therefore, we propose a privacy-preserving electromagnetic-spectrum-sharing trading scheme based on blockchain and ABE. The scheme not only designs an ABE fine-grained access control model in ciphertext form but also constructs a re-encryption algorithm that supports trading arbitration to achieve privacy protection for electromagnetic spectrum trading. Finally, we experimentally evaluated the efficiency of the proposed electromagnetic spectrum trading scheme. The experimental results show that the electromagnetic spectrum trading scheme we propose was highly efficient. Full article

(This article belongs to the Special Issue New Advances in Coding Theory and Cryptography, 2nd Edition)

► Show Figures

Figure 1

15 pages, 1428 KiB

Open AccessArticle

Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging

by Linhui Wang, Jianping Zhu, Chenlu Zheng and Zhiyuan Zhang

Mathematics 2024, 12(18), 2907; https://doi.org/10.3390/math12182907 - 18 Sep 2024

Abstract

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, [...] Read more.

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring. Full article

► Show Figures

Figure 1

25 pages, 1591 KiB

Open AccessArticle

An Attribute-Based End-to-End Policy-Controlled Signcryption Scheme for Secure Group Chat Communication

by Feng Yu, Linghui Meng, Xianxian Li, Daicen Jiang, Weidong Zhu and Zhihua Zeng

Mathematics 2024, 12(18), 2906; https://doi.org/10.3390/math12182906 - 18 Sep 2024

Abstract

Secure instant communication is an important topic of information security. A group chat is a highly convenient mode of instant communication. Increasingly, companies are adopting group chats as a daily office communication tool. However, a large volume of messages in group chat communication [...] Read more.

Secure instant communication is an important topic of information security. A group chat is a highly convenient mode of instant communication. Increasingly, companies are adopting group chats as a daily office communication tool. However, a large volume of messages in group chat communication can lead to message overload, causing group members to miss important information. Additionally, the communication operator’s server may engage in the unreliable behavior of stealing information from the group chat. To address these issues, this paper proposes an attribute-based end-to-end policy-controlled signcryption scheme, aimed at establishing a secure and user-friendly group chat communication mode. By using the linear secret sharing scheme (LSSS) with strong expressive power to construct the access structure in the signcryption technology, the sender can precisely control the recipients of the group chat information to avoid message overload. To minimize computational cost, a signcryption step with constant computational overhead is designed. Additionally, a message-sending mechanism combining “signcryption + encryption” is employed to prevent the operator server from maliciously stealing group chat information. Rigorous analysis shows that PCE-EtoE can resist adaptive chosen-ciphertext attacks under the standard model. Simulation results demonstrate that our theoretical derivation is correct, and that the PCE-EtoE scheme outperforms existing schemes in terms of computational cost, making it suitable for group chat communication. Full article

(This article belongs to the Special Issue Mathematical Methods Applied in Explainable Fake Multimedia Detection)

► Show Figures

Figure 1

17 pages, 5052 KiB

Open AccessArticle

A New Instance Segmentation Model for High-Resolution Remote Sensing Images Based on Edge Processing

by Xiaoying Zhang, Jie Shen, Huaijin Hu and Houqun Yang

Mathematics 2024, 12(18), 2905; https://doi.org/10.3390/math12182905 - 18 Sep 2024

Abstract

With the goal of addressing the challenges of small, densely packed targets in remote sensing images, we propose a high-resolution instance segmentation model named QuadTransPointRend Net (QTPR-Net). This model significantly enhances instance segmentation performance in remote sensing images. The model consists of two [...] Read more.

With the goal of addressing the challenges of small, densely packed targets in remote sensing images, we propose a high-resolution instance segmentation model named QuadTransPointRend Net (QTPR-Net). This model significantly enhances instance segmentation performance in remote sensing images. The model consists of two main modules: preliminary edge feature extraction (PEFE) and edge point feature refinement (EPFR). We also created a specific approach and strategy named TransQTA for edge uncertainty point selection and feature processing in high-resolution remote sensing images. Multi-scale feature fusion and transformer technologies are used in QTPR-Net to refine rough masks and fine-grained features for selected edge uncertainty points while balancing model size and accuracy. Based on experiments performed on three public datasets: NWPU VHR-10, SSDD, and iSAID, we demonstrate the superiority of QTPR-Net over existing approaches. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Machine Learning, 2nd Edition)

► Show Figures

Figure 1

14 pages, 279 KiB

Open AccessFeature PaperArticle

On the Solutions of Linear Systems over Additively Idempotent Semirings

by Álvaro Otero Sánchez, Daniel Camazón Portela and Juan Antonio López-Ramos

Mathematics 2024, 12(18), 2904; https://doi.org/10.3390/math12182904 - 18 Sep 2024

Abstract

The aim of this article is to solve the system

X A = Y

, where

A = (a_{i, j}) \in M_{n \times m} (S)

,

Y \in S^{m}

and X is an unknown vector [...] Read more.

The aim of this article is to solve the system

X A = Y

, where

A = (a_{i, j}) \in M_{n \times m} (S)

,

Y \in S^{m}

and X is an unknown vector of a size n, with S being an additively idempotent semiring. If the system has solutions, then we completely characterize its maximal one, and in the particular case where S is a generalized tropical semiring, a complete characterization of its solutions is provided as well as an explicit bound of the computational cost associated with its computation. Finally, we show how to apply this method to cryptanalyze two different key exchange protocols defined for a finite case and the tropical semiring, respectively. Full article

(This article belongs to the Section Computational and Applied Mathematics)

10 pages, 259 KiB

Open AccessFeature PaperArticle

Modified Cox Models: A Simulation Study on Different Survival Distributions, Censoring Rates, and Sample Sizes

by Iketle Aretha Maharela, Lizelle Fletcher and Ding-Geng Chen

Mathematics 2024, 12(18), 2903; https://doi.org/10.3390/math12182903 - 18 Sep 2024

Abstract

The classical Cox model is the most popular procedure for studying right-censored data in survival analysis. However, it is based on the fundamental assumption of proportional hazards (PH). Modified Cox models, stratified and extended, have been widely employed as solutions when the PH [...] Read more.

The classical Cox model is the most popular procedure for studying right-censored data in survival analysis. However, it is based on the fundamental assumption of proportional hazards (PH). Modified Cox models, stratified and extended, have been widely employed as solutions when the PH assumption is violated. Nevertheless, prior comparisons of the modified Cox models did not employ comprehensive Monte-Carlo simulations to carry out a comparative analysis between the two models. In this paper, we conducted extensive Monte-Carlo simulation to compare the performance of the stratified and extended Cox models under varying censoring rates, sample sizes, and survival distributions. Our results suggest that the models’ performance at varying censoring rates and sample sizes is robust to the distribution of survival times. Thus, their performance under Weibull survival times was comparable to that of exponential survival times. Furthermore, we found that the extended Cox model outperformed other models under every combination of censoring, sample size and survival distribution. Full article

(This article belongs to the Special Issue Statistical Simulation and Computation: 3rd Edition)

24 pages, 297 KiB

Open AccessArticle

Novel Classes on Generating Functions of the Products of (p,q)-Modified Pell Numbers with Several Bivariate Polynomials

by Ali Boussayoud, Salah Boulaaras and Ali Allahem

Mathematics 2024, 12(18), 2902; https://doi.org/10.3390/math12182902 - 18 Sep 2024

Abstract

In this paper, using the symmetrizing operator

δ_{e_{1} e_{2}}^{2 - l}

, we derive new generating functions of the products of

(p, q)

-modified Pell numbers with various bivariate polynomials, including Mersenne and Mersenne Lucas polynomials, Fibonacci and [...] Read more.

In this paper, using the symmetrizing operator

δ_{e_{1} e_{2}}^{2 - l}

, we derive new generating functions of the products of

(p, q)

-modified Pell numbers with various bivariate polynomials, including Mersenne and Mersenne Lucas polynomials, Fibonacci and Lucas polynomials, bivariate Pell and bivariate Pell Lucas polynomials, bivariate Jacobsthal and bivariate Jacobsthal Lucas polynomials, bivariate Vieta–Fibonacci and bivariate Vieta–Lucas polynomials, and bivariate complex Fibonacci and bivariate complex Lucas polynomials. Full article

20 pages, 1177 KiB

Open AccessArticle

AssocKD: An Association-Aware Knowledge Distillation Method for Document-Level Event Argument Extraction

by Lijun Tan, Yanli Hu, Jianwei Cao and Zhen Tan

Mathematics 2024, 12(18), 2901; https://doi.org/10.3390/math12182901 - 18 Sep 2024

Abstract

Event argument extraction is a crucial subtask of event extraction, which aims at extracting arguments that correspond to argument roles when given event types. The majority of current document-level event argument extraction works focus on extracting information for only one event at a [...] Read more.

Event argument extraction is a crucial subtask of event extraction, which aims at extracting arguments that correspond to argument roles when given event types. The majority of current document-level event argument extraction works focus on extracting information for only one event at a time without considering the association among events; this is known as document-level single-event extraction. However, the interrelationship among arguments can yield mutual gains in their extraction. Therefore, in this paper, we propose AssocKD, an Association-aware Knowledge Distillation Method for Document-level Event Argument Extraction, which enables the enhancement of document-level multi-event extraction with event association knowledge. Firstly, we introduce an association-aware training task to extract unknown arguments with the given privileged knowledge of relevant arguments, obtaining an association-aware model that can construct both intra-event and inter-event relationships. Secondly, we adopt multi-teacher knowledge distillation to transfer such event association knowledge from the association-aware teacher models to the event argument extraction student model. Our proposed method, AssocKD, is capable of explicitly modeling and efficiently leveraging event association to enhance the extraction of multi-event arguments at the document level. We conduct experiments on RAMS and WIKIEVENTS datasets and observe a significant improvement, thus demonstrating the effectiveness of our method. Full article

► Show Figures

Figure 1

24 pages, 5101 KiB

Open AccessArticle

Evolutionary Game and Simulation Analysis of New-Energy Vehicle Promotion in China Based on Reward and Punishment Mechanisms

by Rongjiang Cai, Tao Zhang, Xi Wang, Qiaoran Jia, Shufang Zhao, Nana Liu and Xiaoguang Wang

Mathematics 2024, 12(18), 2900; https://doi.org/10.3390/math12182900 - 18 Sep 2024

Abstract

In China, new-energy vehicles are viewed as the ultimate goal for the automobile industry, given the current focus on the “dual-carbon” target. Therefore, it is important to promote the sustainable development of this new-energy market and ensure a smooth transition from fuel-driven vehicles [...] Read more.

In China, new-energy vehicles are viewed as the ultimate goal for the automobile industry, given the current focus on the “dual-carbon” target. Therefore, it is important to promote the sustainable development of this new-energy market and ensure a smooth transition from fuel-driven vehicles to new-energy vehicles. This study constructs a tripartite evolutionary game model involving vehicle enterprises, consumers, and the government. It improves the tripartite evolutionary game through the mechanisms of dynamic and static rewards and punishments, respectively, using real-world data. The results show the following. (1) A fluctuation is present in the sales of new-energy vehicles by enterprises and the active promotional behavior of the government. This fluctuation leads to instability, and the behavior is difficult to accurately predict, which is not conducive new-energy vehicles’ promotion and sales. (2) A static reward and punishment mechanism can change the fluctuation threshold or peak value. Nevertheless, the stability of the system’s strategy is not the main reason that the government has been actively promoting it for a long time. However, enterprises are still wavering between new-energy and fuel vehicles. (3) The linear dynamic reward and punishment mechanism also has its defects. Although they are considered the stability control strategy of the system, they are still not conducive to stability. (4) The nonlinear dynamic reward and punishment mechanism can help the system to achieve the ideal stabilization strategy. Full article

► Show Figures

Figure 1

29 pages, 9761 KiB

Open AccessArticle

High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia

by I Gede Nyoman Mindra Jaya and Henk Folmer

Mathematics 2024, 12(18), 2899; https://doi.org/10.3390/math12182899 - 17 Sep 2024

Abstract

Accurate forecasting of high-resolution particulate matter 2.5 (PM_2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem. The first stage [...] Read more.

Accurate forecasting of high-resolution particulate matter 2.5 (PM_2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem. The first stage is a multivariate spatial time series (MSTS) model, used to generate forecasts for the sampled spatial units and to impute missing observations. The MSTS model utilizes the similarities between the temporal patterns of the time series of the spatial units to impute the missing data across space. The second stage is the high-resolution prediction model, which generates predictions that cover the entire study domain. The second stage faces the big N problem giving rise to complex memory and computational problems. As a solution to the big N problem, we propose a Gaussian Markov random field (GMRF) for innovations with the Matérn covariance matrix obtained from the corresponding Gaussian field (GF) matrix by means of the stochastic partial differential equation (SPDE) method and the finite element method (FEM). For inference, we propose Bayesian statistics and integrated nested Laplace approximation (INLA) in the R-INLA package. The above approach is demonstrated using daily data collected from 13 PM_2.5 monitoring stations in Jakarta Province, Indonesia, for 1 January–31 December 2022. The first stage of the model generates PM_2.5 forecasts for the 13 monitoring stations for the period 1–31 January 2023, imputing missing data by means of the MSTS model. To capture temporal trends in the PM_2.5 concentrations, the model applies a first-order autoregressive process and a seasonal process. The second stage involves creating a high-resolution map for the period 1–31 January 2023, for sampled and non-sampled spatiotemporal units. It uses the MSTS-generated PM_2.5 predictions for the sampled spatiotemporal units and observations of the covariate’s altitude, population density, and rainfall for sampled and non-samples spatiotemporal units. For the spatially correlated random effects, we apply a first-order random walk process. The validation of out-of-sample forecasts indicates a strong model fit with low mean squared error (0.001), mean absolute error (0.037), and mean absolute percentage error (0.041), and a high R² value (0.855). The analysis reveals that altitude and precipitation negatively impact PM_2.5 concentrations, while population density has a positive effect. Specifically, a one-meter increase in altitude is linked to a 7.8% decrease in PM_2.5, while a one-person increase in population density leads to a 7.0% rise in PM_2.5. Additionally, a one-millimeter increase in rainfall corresponds to a 3.9% decrease in PM_2.5. The paper makes a valuable contribution to the field of forecasting high-resolution PM_2.5 levels, which is essential for providing detailed, accurate information for public health policy. The approach presents a new and innovative method for addressing the problem of missing data and high-resolution forecasting. Full article

(This article belongs to the Special Issue Advanced Statistical Application for Realistic Problems)

► Show Figures

Figure 1

Journal Description

Mathematics

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI