Mathematics最新文献_第8页

Stability and Hopf Bifurcation Analysis of a Predator–Prey Model with Weak Allee Effect Delay and Competition Delay 具有弱阿利效应延迟和竞争延迟的捕食者-猎物模型的稳定性和霍普夫分岔分析

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-13 DOI: 10.3390/math12182853

Yurong Dong, Hua Liu, Yumei Wei, Qibin Zhang, Gang Ma

引用次数: 0

Third-Order Noncanonical Neutral Delay Differential Equations: Nonexistence of Kneser Solutions via Myshkis Type Criteria 三阶非正则中性延迟微分方程：通过 Myshkis 型准则的 Kneser 解决方案的不存在性

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-13 DOI: 10.3390/math12182847

Gunasekaran Nithyakala, George E. Chatzarakis, Govindasamy Ayyappan, Ethiraju Thandapani

引用次数: 0

Efficient Study on Westervelt-Type Equations to Design Metamaterials via Symmetry Analysis 通过对称分析设计超材料的 Westervelt 型方程的高效研究

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-13 DOI: 10.3390/math12182855

Zehra Pinar Izgi, Pshtiwan Othman Mohammed, Ravi P. Agarwal, Majeed A. Yousif, Alina Alb Lupas, Mohamed Abdelwahed

引用次数: 0

A Particle Swarm Optimization-Based Interpretable Spiking Neural Classifier with Time-Varying Weights 基于粒子群优化的时变权重可解释尖峰神经分类器

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-13 DOI: 10.3390/math12182846

Mohammed Thousif, Shirin Dora, Suresh Sundaram

{"title":"A Particle Swarm Optimization-Based Interpretable Spiking Neural Classifier with Time-Varying Weights","authors":"Mohammed Thousif, Shirin Dora, Suresh Sundaram","doi":"10.3390/math12182846","DOIUrl":"https://doi.org/10.3390/math12182846","url":null,"abstract":"This paper presents an interpretable, spiking neural classifier (IpT-SNC) with time-varying weights. IpT-SNC uses a two-layered spiking neural network (SNN) architecture in which weights of synapses are modeled using amplitude-modulated, time-varying Gaussian functions. Self-regulated particle swarm optimization (SRPSO) is used to update the amplitude, width, and centers of the Gaussian functions and thresholds of neurons in the output layer. IpT-SNC has been developed to improve the interpretability of spiking neural networks. The time-varying weights in IpT-SNC allow us to describe the rationale behind predictions in terms of specific input spikes. The performance of IpT-SNC is evaluated on ten benchmark datasets in the UCI machine learning repository and compared with the performance of other learning algorithms. According to the performance results, IpT-SNC enhances classification performance on testing datasets from a minimum of 0.5% to a maximum of 7.7%. The significance level of IpT-SNC with other learning algorithms is evaluated using statistical tests like the Friedman test and the paired t-test. Furthermore, on the challenging real-world BCI (Brain Computer Interface) competition IV dataset, IpT-SNC outperforms current classifiers by about 8% in terms of classification accuracy. The results indicate that IpT-SNC has better generalization performance than other algorithms.","PeriodicalId":18303,"journal":{"name":"Mathematics","volume":"22 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Tourist Trip Design Problem with Stochastic Travel Times and Partial Charging for Battery Electric Vehicles 具有随机旅行时间和电池电动汽车部分充电功能的新型旅游行程设计问题

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-11 DOI: 10.3390/math12182822

Samita Kedkaew, Warisa Nakkiew, Parida Jewpanya, Wasawat Nakkiew

{"title":"A Novel Tourist Trip Design Problem with Stochastic Travel Times and Partial Charging for Battery Electric Vehicles","authors":"Samita Kedkaew, Warisa Nakkiew, Parida Jewpanya, Wasawat Nakkiew","doi":"10.3390/math12182822","DOIUrl":"https://doi.org/10.3390/math12182822","url":null,"abstract":"This study proposes a novel mathematical model for the Multi-Day Tourist Trip Design Problem with Stochastic Travel Time and Partial Charging for Battery Electric Vehicle (MD-TTDP-STT-PCBEV). To the best of our knowledge, no prior study has fully incorporated the use of BEVs into TTDP models. Given the limited driving range of BEVs, the model requires decisions regarding the locations and policy for recharging the vehicle’s battery. The problem also incorporates real-world uncertainty by considering travel time as a random variable subjected to normal distribution. The model is formulated using chance-constraint programming, aiming to find optimal tourist routes for BEVs that maximize tourist satisfaction. Numerical experiments were conducted to compare solutions between stochastic and deterministic environments. Computational experiments using the LINGO optimization solver demonstrated that the total rating scores obtained from the stochastic model with chance-constraint programming were generally lower than those from the deterministic model due to travel time uncertainties. These results highlight the importance of incorporating real-world uncertainty and variability to achieve more accurate and reliable planning.","PeriodicalId":18303,"journal":{"name":"Mathematics","volume":"9 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rigid Polynomial Differential Systems with Homogeneous Nonlinearities 具有同质非线性的刚性多项式微分系统

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-11 DOI: 10.3390/math12182806

Jaume Llibre

引用次数: 0

Optimal Epidemic Control with Nonmedical and Medical Interventions 利用非医疗和医疗干预措施优化流行病控制

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-11 DOI: 10.3390/math12182811

Alexandra Smirnova, Mona Baroonian, Xiaojing Ye

{"title":"Optimal Epidemic Control with Nonmedical and Medical Interventions","authors":"Alexandra Smirnova, Mona Baroonian, Xiaojing Ye","doi":"10.3390/math12182811","DOIUrl":"https://doi.org/10.3390/math12182811","url":null,"abstract":"In this study, we investigate different epidemic control scenarios through theoretical analysis and numerical simulations. To account for two important types of control at the early ascending stage of an outbreak, nonmedical interventions, and medical treatments, a compartmental model is considered with the first control aimed at lowering the disease transmission rate through behavioral changes and the second control set to lower the period of infectiousness by means of antiviral medications and other forms of medical care. In all experiments, the implementation of control strategies reduces the daily cumulative number of cases and successfully “flattens the curve”. The reduction in the cumulative cases is achieved by eliminating or delaying new cases. This delay is incredibly valuable, as it provides public health organizations with more time to advance antiviral treatments and devise alternative preventive measures. The main theoretical result of the paper, Theorem 1, concludes that the two optimal control functions may be increasing initially. However, beyond a certain point, both controls decline (possibly causing the number of newly infected people to grow). The numerical simulations conducted by the authors confirm theoretical findings, which indicates that, ideally, around the time that early interventions become less effective, the control strategy must be upgraded through the addition of new and improved tools, such as vaccines, therapeutics, testing, air ventilation, and others, in order to successfully battle the virus going forward.","PeriodicalId":18303,"journal":{"name":"Mathematics","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Predictive Models for On-Street Parking Occupancy: Integrating Adaptive GCN and GRU with Household Categories and POI Factors 增强路边停车位占用率预测模型：将自适应 GCN 和 GRU 与家庭类别和 POI 因素相结合

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-11 DOI: 10.3390/math12182823

Xiaohang Zhao, Mingyuan Zhang

{"title":"Enhancing Predictive Models for On-Street Parking Occupancy: Integrating Adaptive GCN and GRU with Household Categories and POI Factors","authors":"Xiaohang Zhao, Mingyuan Zhang","doi":"10.3390/math12182823","DOIUrl":"https://doi.org/10.3390/math12182823","url":null,"abstract":"Accurate predictions of parking occupancy are vital for navigation and autonomous transport systems. This research introduces a deep learning mode, AGCRU, which integrates Adaptive Graph Convolutional Networks (GCNs) with Gated Recurrent Units (GRUs) for predicting on-street parking occupancy. By leveraging real-world data from Melbourne, the proposed model utilizes on-street parking sensors to capture both temporal and spatial dynamics of parking behaviors. The AGCRU model is enhanced with the inclusion of Points of Interest (POIs) and housing data to refine its predictive accuracy based on spatial relationships and parking habits. Notably, the model demonstrates a mean absolute error (MAE) of 0.0156 at 15 min, 0.0330 at 30 min, and 0.0558 at 60 min; root mean square error (RMSE) values are 0.0244, 0.0665, and 0.1003 for these intervals, respectively. The mean absolute percentage error (MAPE) for these intervals is 1.5561%, 3.3071%, and 5.5810%. These metrics, considerably lower than those from traditional and competing models, indicate the high efficiency and accuracy of the AGCRU model in an urban setting. This demonstrates the model as a tool for enhancing urban parking management and planning strategies.","PeriodicalId":18303,"journal":{"name":"Mathematics","volume":"1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Next-Generation Diagnostics: The Impact of Synthetic Data Generation on the Detection of Breast Cancer from Ultrasound Imaging 下一代诊断技术：合成数据生成对通过超声波成像检测乳腺癌的影响

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-11 DOI: 10.3390/math12182808

Hari Mohan Rai, Serhii Dashkevych, Joon Yoo

{"title":"Next-Generation Diagnostics: The Impact of Synthetic Data Generation on the Detection of Breast Cancer from Ultrasound Imaging","authors":"Hari Mohan Rai, Serhii Dashkevych, Joon Yoo","doi":"10.3390/math12182808","DOIUrl":"https://doi.org/10.3390/math12182808","url":null,"abstract":"Breast cancer is one of the most lethal and widespread diseases affecting women worldwide. As a result, it is necessary to diagnose breast cancer accurately and efficiently utilizing the most cost-effective and widely used methods. In this research, we demonstrated that synthetically created high-quality ultrasound data outperformed conventional augmentation strategies for efficiently diagnosing breast cancer using deep learning. We trained a deep-learning model using the EfficientNet-B7 architecture and a large dataset of 3186 ultrasound images acquired from multiple publicly available sources, as well as 10,000 synthetically generated images using generative adversarial networks (StyleGAN3). The model was trained using five-fold cross-validation techniques and validated using four metrics: accuracy, recall, precision, and the F1 score measure. The results showed that integrating synthetically produced data into the training set increased the classification accuracy from 88.72% to 92.01% based on the F1 score, demonstrating the power of generative models to expand and improve the quality of training datasets in medical-imaging applications. This demonstrated that training the model using a larger set of data comprising synthetic images significantly improved its performance by more than 3% over the genuine dataset with common augmentation. Various data augmentation procedures were also investigated to improve the training set’s diversity and representativeness. This research emphasizes the relevance of using modern artificial intelligence and machine-learning technologies in medical imaging by providing an effective strategy for categorizing ultrasound images, which may lead to increased diagnostic accuracy and optimal treatment options. The proposed techniques are highly promising and have strong potential for future clinical application in the diagnosis of breast cancer.","PeriodicalId":18303,"journal":{"name":"Mathematics","volume":"7 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Orlicz Spaces and Their Hyperbolic Composition Operators 奥利兹空间及其双曲合成算子

IF 2.4 3区数学

Mathematics Pub Date : 2024-09-11 DOI: 10.3390/math12182809

Mohammed Said Al Ghafri, Yousef Estaremi, Zhidong Huang

引用次数: 0