Mathematical Biosciences and Engineering最新文献_第3页

Fairy circles and temporal periodic patterns in the delayed plant-sulfide feedback model. 延迟植物-硫化物反馈模型中的仙女圈和时间周期模式。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-08-07 DOI: 10.3934/mbe.2024297

Xin Wei, Jianjun Paul Tian, Jiantao Zhao

{"title":"Fairy circles and temporal periodic patterns in the delayed plant-sulfide feedback model.","authors":"Xin Wei, Jianjun Paul Tian, Jiantao Zhao","doi":"10.3934/mbe.2024297","DOIUrl":"https://doi.org/10.3934/mbe.2024297","url":null,"abstract":"Incorporating the self-regulatory mechanism with time delay to a plant-sulfide feedback system for intertidal salt marshes, we proposed and studied a functional reaction-diffusion model. We analyzed the stability of the positive steady state of the system, and derived the sufficient conditions for the occurrence of Hopf bifurcations. By deriving the normal form on the center manifold, we obtained the formulas determining the properties of the Hopf bifurcations. Our analysis showed that there is a critical value of time delay. When the time delay is greater than the critical value, the system will show asymptotical temporal periodic patterns while the system will display asymptotical spatial homogeneous patterns when the time delay is smaller than the critical value. Our numerical study showed that there are transient fairy circles for any time delay while there are different types of fairy circles and rings in the system. Our results enhance the concept that transient fairy circle patterns in intertidal salt marshes can infer the underlying ecological mechanisms and provide a measure of ecological resilience when the self-regulatory mechanism with time delay is considered.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Refined matrix completion for spectrum estimation of heart rate variability. 用于心率变异性频谱估计的改进矩阵补全。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-08-02 DOI: 10.3934/mbe.2024296

Lei Lu, Tingting Zhu, Ying Tan, Jiandong Zhou, Jenny Yang, Lei Clifton, Yuan-Ting Zhang, David A Clifton

引用次数: 0

Migration and proliferation drive the emergence of patterns in co-cultures of differentiating vascular progenitor cells. 迁移和增殖推动了分化血管祖细胞共培养模式的出现。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-08-01 DOI: 10.3934/mbe.2024295

Jose E Zamora Alvarado, Kara E McCloskey, Ajay Gopinathan

{"title":"Migration and proliferation drive the emergence of patterns in co-cultures of differentiating vascular progenitor cells.","authors":"Jose E Zamora Alvarado, Kara E McCloskey, Ajay Gopinathan","doi":"10.3934/mbe.2024295","DOIUrl":"10.3934/mbe.2024295","url":null,"abstract":"Vascular cells self-organize into unique structures guided by cell proliferation, migration, and/or differentiation from neighboring cells, mechanical factors, and/or soluble signals. However, the relative contribution of each of these factors remains unclear. Our objective was to develop a computational model to explore the different factors affecting the emerging micropatterns in 2D. This was accomplished by developing a stochastic on-lattice population-based model starting with vascular progenitor cells with the potential to proliferate, migrate, and/or differentiate into either endothelial cells or smooth muscle cells. The simulation results yielded patterns that were qualitatively and quantitatively consistent with experimental observations. Our results suggested that post-differentiation cell migration and proliferation when balanced could generate between 30-70% of each cell type enabling the formation of vascular patterns. Moreover, the cell-to-cell sensing could enhance the robustness of this patterning. These findings computationally supported that 2D patterning is mechanistically similar to current microfluidic platforms that take advantage of the migration-directed self-assembly of mature endothelial and mural cells to generate perfusable 3D vasculature in permissible hydrogel environments and suggest that stem or progenitor cells may not be fully necessary components in many tissue formations like those formed by vasculogenesis.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11556463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CTFusion: CNN-transformer-based self-supervised learning for infrared and visible image fusion. CTFusion：基于 CNN 变换器的自监督学习，用于红外和可见光图像融合。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-30 DOI: 10.3934/mbe.2024294

Keying Du, Liuyang Fang, Jie Chen, Dongdong Chen, Hua Lai

{"title":"CTFusion: CNN-transformer-based self-supervised learning for infrared and visible image fusion.","authors":"Keying Du, Liuyang Fang, Jie Chen, Dongdong Chen, Hua Lai","doi":"10.3934/mbe.2024294","DOIUrl":"https://doi.org/10.3934/mbe.2024294","url":null,"abstract":"Infrared and visible image fusion (IVIF) is devoted to extracting and integrating useful complementary information from muti-modal source images. Current fusion methods usually require a large number of paired images to train the models in supervised or unsupervised way. In this paper, we propose CTFusion, a convolutional neural network (CNN)-Transformer-based IVIF framework that uses self-supervised learning. The whole framework is based on an encoder-decoder network, where encoders are endowed with strong local and global dependency modeling ability via the CNN-Transformer-based feature extraction (CTFE) module design. Thanks to the development of self-supervised learning, the model training does not require ground truth fusion images with simple pretext task. We designed a mask reconstruction task according to the characteristics of IVIF, through which the network can learn the characteristics of both infrared and visible images and extract more generalized features. We evaluated our method and compared it to five competitive traditional and deep learning-based methods on three IVIF benchmark datasets. Extensive experimental results demonstrate that our CTFusion can achieve the best performance compared to the state-of-the-art methods in both subjective and objective evaluations.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video-based person re-identification with complementary local and global features using a graph transformer. 使用图变换器，利用互补的局部和全局特征进行基于视频的人物再识别。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-23 DOI: 10.3934/mbe.2024293

Hai Lu, Enbo Luo, Yong Feng, Yifan Wang

{"title":"Video-based person re-identification with complementary local and global features using a graph transformer.","authors":"Hai Lu, Enbo Luo, Yong Feng, Yifan Wang","doi":"10.3934/mbe.2024293","DOIUrl":"https://doi.org/10.3934/mbe.2024293","url":null,"abstract":"In recent years, significant progress has been made in video-based person re-identification (Re-ID). The key challenge in video person Re-ID lies in effectively constructing discriminative and robust person feature representations. Methods based on local regions utilize spatial and temporal attention to extract representative local features. However, prior approaches often overlook the correlations between local regions. To leverage relationships among different local regions, we have proposed a novel video person Re-ID representation learning approach based on a graph transformer, which facilitates contextual interactions between relevant region features. Specifically, we construct a local relation graph to model intrinsic relationships between nodes representing local regions. This graph employs the architecture of a transformer for feature propagation, iteratively refining region features and considering information from adjacent nodes to obtain partial feature representations. To learn compact and discriminative representations, we have further proposed a global feature learning branch based on a vision transformer to capture the relationships between different frames in a sequence. Additionally, we designed a dual-branch interaction network based on multi-head fusion attention to integrate frame-level features extracted by both local and global branches. Finally, the concatenated global and local features, after interaction, are used for testing. We evaluated the proposed method on three datasets, namely iLIDS-VID, MARS, and DukeMTMC-VideoReID. Experimental results demonstrate competitive performance, validating the effectiveness of our proposed approach.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development. 肿瘤自由生长建模：解读癌症发展的离散、连续和混合方法。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-19 DOI: 10.3934/mbe.2024292

Dashmi Singh, Dana Paquin

引用次数: 0

Retraction notice to "A video images-aware knowledge extraction method for intelligent healthcare management of basketball players" [Mathematical Biosciences and Engineering 20(2) (2023) 1919-1937]. 用于篮球运动员智能健康管理的视频图像感知知识提取方法"[《数学生物科学与工程》20（2）（2023）1919-1937]的撤稿通知。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-18 DOI: 10.3934/mbe.2024291

Editorial Office Of Mathematical Biosciences And Engineering

引用次数: 0

Improved optimizer with deep learning model for emotion detection and classification. 利用深度学习模型改进优化情绪检测和分类。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-17 DOI: 10.3934/mbe.2024290

C Willson Joseph, G Jaspher Willsie Kathrine, Shanmuganathan Vimal, S Sumathi, Danilo Pelusi, Xiomara Patricia Blanco Valencia, Elena Verdú

{"title":"Improved optimizer with deep learning model for emotion detection and classification.","authors":"C Willson Joseph, G Jaspher Willsie Kathrine, Shanmuganathan Vimal, S Sumathi, Danilo Pelusi, Xiomara Patricia Blanco Valencia, Elena Verdú","doi":"10.3934/mbe.2024290","DOIUrl":"https://doi.org/10.3934/mbe.2024290","url":null,"abstract":"Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A topical VAEGAN-IHMM approach for automatic story segmentation. 用于自动故事分割的专题 VAEGAN-IHMM 方法。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-16 DOI: 10.3934/mbe.2024289

Jia Yu, Huiling Peng, Guoqiang Wang, Nianfeng Shi

{"title":"A topical VAEGAN-IHMM approach for automatic story segmentation.","authors":"Jia Yu, Huiling Peng, Guoqiang Wang, Nianfeng Shi","doi":"10.3934/mbe.2024289","DOIUrl":"https://doi.org/10.3934/mbe.2024289","url":null,"abstract":"Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stochastic assessment of temperature-CO2 causal relationship in climate from the Phanerozoic through modern times. 从新生代到现代气候中温度-二氧化碳因果关系的随机评估。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-07-10 DOI: 10.3934/mbe.2024287

Demetris Koutsoyiannis

{"title":"Stochastic assessment of temperature-CO2 causal relationship in climate from the Phanerozoic through modern times.","authors":"Demetris Koutsoyiannis","doi":"10.3934/mbe.2024287","DOIUrl":"https://doi.org/10.3934/mbe.2024287","url":null,"abstract":"As a result of recent research, a new stochastic methodology of assessing causality was developed. Its application to instrumental measurements of temperature (T) and atmospheric carbon dioxide concentration ([CO2]) over the last seven decades provided evidence for a unidirectional, potentially causal link between T as the cause and [CO2] as the effect. Here, I refine and extend this methodology and apply it to both paleoclimatic proxy data and instrumental data of T and [CO2]. Several proxy series, extending over the Phanerozoic or parts of it, gradually improving in accuracy and temporal resolution up to the modern period of accurate records, are compiled, paired, and analyzed. The extensive analyses made converge to the single inference that change in temperature leads, and that in carbon dioxide concentration lags. This conclusion is valid for both proxy and instrumental data in all time scales and time spans. The time scales examined begin from annual and decadal for the modern period (instrumental data) and the last two millennia (proxy data), and reach one million years for the most sparse time series for the Phanerozoic. The type of causality appears to be unidirectional, T→[CO2], as in earlier studies. The time lags found depend on the time span and time scale and are of the same order of magnitude as the latter. These results contradict the conventional wisdom, according to which the temperature rise is caused by [CO2] increase.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0