Mathematical Biosciences and Engineering最新文献_第10页

Numerical and graphical simulation of the non-linear fractional dynamical system of bone mineralization. 骨矿化非线性分数动力系统的数值和图形模拟。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-04 DOI: 10.3934/mbe.2024227

Ritu Agarwal, Pooja Airan, Mohammad Sajid

引用次数: 0

A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review. 基于双人神经科学的无创脑刺激双脑治疗方法：观点综述。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-04 DOI: 10.3934/mbe.2024226

Naoyuki Takeuchi

{"title":"A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review.","authors":"Naoyuki Takeuchi","doi":"10.3934/mbe.2024226","DOIUrl":"10.3934/mbe.2024226","url":null,"abstract":"Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning-a neuroimaging technique that simultaneously measures the activity of multiple brain regions-has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318752","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

The dynamics and control of a multi-lingual rumor propagation model with non-smooth inhibition mechanism. 具有非平滑抑制机制的多语言谣言传播模型的动态和控制。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-04 DOI: 10.3934/mbe.2024224

Jing Liao, Jinling Wang, Jiarong Li, Xin Jiang

引用次数: 0

A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection. 在青光眼检测中同时进行视杯和视盘分割的新型轻量级深度学习方法。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-04 DOI: 10.3934/mbe.2024225

Yantao Song, Wenjie Zhang, Yue Zhang

引用次数: 0

Predefined-time sliding mode control of chaotic systems based on disturbance observer. 基于扰动观测器的混沌系统预定义时间滑模控制。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-04 DOI: 10.3934/mbe.2024222

Yun Liu, Yuhong Huo

引用次数: 0

Sparse-view X-ray CT based on a box-constrained nonlinear weighted anisotropic TV regularization. 基于盒约束非线性加权各向异性 TV 正则化的稀疏视图 X 射线 CT。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-04 DOI: 10.3934/mbe.2024223

Huiying Li, Yizhuang Song

{"title":"Sparse-view X-ray CT based on a box-constrained nonlinear weighted anisotropic TV regularization.","authors":"Huiying Li, Yizhuang Song","doi":"10.3934/mbe.2024223","DOIUrl":"https://doi.org/10.3934/mbe.2024223","url":null,"abstract":"Sparse-view computed tomography (CT) is an important way to reduce the negative effect of radiation exposure in medical imaging by skipping some X-ray projections. However, due to violating the Nyquist/Shannon sampling criterion, there are severe streaking artifacts in the reconstructed CT images that could mislead diagnosis. Noting the ill-posedness nature of the corresponding inverse problem in a sparse-view CT, minimizing an energy functional composed by an image fidelity term together with properly chosen regularization terms is widely used to reconstruct a medical meaningful attenuation image. In this paper, we propose a regularization, called the box-constrained nonlinear weighted anisotropic total variation (box-constrained NWATV), and minimize the regularization term accompanying the least square fitting using an alternative direction method of multipliers (ADMM) type method. The proposed method is validated through the Shepp-Logan phantom model, alongisde the actual walnut X-ray projections provided by Finnish Inverse Problems Society and the human lung images. The experimental results show that the reconstruction speed of the proposed method is significantly accelerated compared to the existing $ L_1/L_2 $ regularization method. Precisely, the central processing unit (CPU) time is reduced more than 8 times.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318803","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

Cross-modal missing time-series imputation using dense spatio-temporal transformer nets. 利用密集时空变换网进行跨模态缺失时间序列估算。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-01 DOI: 10.3934/mbe.2024220

Xusheng Qian, Teng Zhang, Meng Miao, Gaojun Xu, Xuancheng Zhang, Wenwu Yu, Duxin Chen

{"title":"Cross-modal missing time-series imputation using dense spatio-temporal transformer nets.","authors":"Xusheng Qian, Teng Zhang, Meng Miao, Gaojun Xu, Xuancheng Zhang, Wenwu Yu, Duxin Chen","doi":"10.3934/mbe.2024220","DOIUrl":"https://doi.org/10.3934/mbe.2024220","url":null,"abstract":"Due to irregular sampling or device failure, the data collected from sensor network has missing value, that is, missing time-series data occurs. To address this issue, many methods have been proposed to impute random or non-random missing data. However, the imputation accuracy of these methods are not accurate enough to be applied, especially in the case of complete data missing (CDM). Thus, we propose a cross-modal method to impute time-series missing data by dense spatio-temporal transformer nets (DSTTN). This model embeds spatial modal data into time-series data by stacked spatio-temporal transformer blocks and deployment of dense connections. It adopts cross-modal constraints, a graph Laplacian regularization term, to optimize model parameters. When the model is trained, it recovers missing data finally by an end-to-end imputation pipeline. Various baseline models are compared by sufficient experiments. Based on the experimental results, it is verified that DSTTN achieves state-of-the-art imputation performance in the cases of random and non-random missing. Especially, the proposed method provides a new solution to the CDM problem.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318768","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

Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism. 基于多维卷积和注意力机制的轴承故障检测。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-01 DOI: 10.3934/mbe.2024216

Yingying Xu, Chunhe Song, Chu Wang

{"title":"Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.","authors":"Yingying Xu, Chunhe Song, Chu Wang","doi":"10.3934/mbe.2024216","DOIUrl":"https://doi.org/10.3934/mbe.2024216","url":null,"abstract":"Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318774","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 leader-following consensus of multi-agent systems with actuator saturation and semi-Markov switching topologies. 具有执行器饱和和半马尔可夫开关拓扑的多代理系统的领导者-跟随共识。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-01 DOI: 10.3934/mbe.2024217

Jiangtao Dai, Ge Guo

引用次数: 0

Micro-expression recognition based on multi-scale 3D residual convolutional neural network. 基于多尺度三维残差卷积神经网络的微表情识别。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-03-01 DOI: 10.3934/mbe.2024221

Hongmei Jin, Ning He, Zhanli Li, Pengcheng Yang

{"title":"Micro-expression recognition based on multi-scale 3D residual convolutional neural network.","authors":"Hongmei Jin, Ning He, Zhanli Li, Pengcheng Yang","doi":"10.3934/mbe.2024221","DOIUrl":"https://doi.org/10.3934/mbe.2024221","url":null,"abstract":"In demanding application scenarios such as clinical psychotherapy and criminal interrogation, the accurate recognition of micro-expressions is of utmost importance but poses significant challenges. One of the main difficulties lies in effectively capturing weak and fleeting facial features and improving recognition performance. To address this fundamental issue, this paper proposed a novel architecture based on a multi-scale 3D residual convolutional neural network. The algorithm leveraged a deep 3D-ResNet50 as the skeleton model and utilized the micro-expression optical flow feature map as the input for the network model. Drawing upon the complex spatial and temporal features inherent in micro-expressions, the network incorporated multi-scale convolutional modules of varying sizes to integrate both global and local information. Furthermore, an attention mechanism feature fusion module was introduced to enhance the model's contextual awareness. Finally, to optimize the model's prediction of the optimal solution, a discriminative network structure with multiple output channels was constructed. The algorithm's performance was evaluated using the public datasets SMIC, SAMM, and CASME Ⅱ. The experimental results demonstrated that the proposed algorithm achieves recognition accuracies of 74.6, 84.77 and 91.35% on these datasets, respectively. This substantial improvement in efficiency compared to existing mainstream methods for extracting micro-expression subtle features effectively enhanced micro-expression recognition performance and increased the accuracy of high-precision micro-expression recognition. Consequently, this paper served as an important reference for researchers working on high-precision micro-expression recognition.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318779","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