Mathematical Biosciences and Engineering最新文献

Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic process. Traditional methods fail to extract multiscale information, discard unnecessary information, and delineate thin vessels. In this paper, a novel residual U-Net architecture that incorporates multi-scale feature learning and effective attention is proposed to delineate the retinal vessels precisely. Since drop block regularization performs better than drop out in preventing overfitting, drop block was used in this study. A multi-scale feature learning module was added instead of a skip connection to learn multi-scale features. A novel effective attention block was proposed and integrated with the decoder block to obtain precise spatial and channel information. Experimental findings indicated that the proposed model exhibited outstanding performance in retinal vessel delineation. The sensitivities achieved for DRIVE, STARE, and CHASE_DB datasets were 0.8293, 0.8151 and 0.8084, respectively.

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis.

Facial age recognition has been widely used in real-world applications. Most of current facial age recognition methods use deep learning to extract facial features to identify age. However, due to the high dimension features of faces, deep learning methods might extract a lot of redundant features, which is not beneficial for facial age recognition. To improve facial age recognition effectively, this paper proposed the deep manifold learning (DML), a combination of deep learning and manifold learning. In DML, deep learning was used to extract high-dimensional facial features, and manifold learning selected age-related features from these high-dimensional facial features for facial age recognition. Finally, we validated the DML on Multivariate Observations of Reactions and Physical Health (MORPH) and Face and Gesture Recognition Network (FG-NET) datasets. The results indicated that the mean absolute error (MAE) of MORPH is 1.60 and that of FG-NET is 2.48. Moreover, compared with the state of the art facial age recognition methods, the accuracy of DML has been greatly improved.

The vegetation pattern generated by aeolian sand movements is a typical type of vegetation patterns in arid and semi-arid areas. This paper presents a vegetation-sand model with nonlocal interaction characterized by an integral term with a kernel function. The instability of the Turing pattern was analyzed and the conditions of stable pattern occurrence were obtained. At the same time, the multiple scales method was applied to obtain the amplitude equations at the critical value of Turing bifurcation. The spatial distributions of vegetation under different delays were obtained by numerical simulation. The results revealed that the vegetation biomass increased as the interaction intensity decreased or as the nonlocal interaction distance increased. We demonstrated that the nonlocal interaction between vegetation and sand is a crucial mechanism for forming vegetation patterns, which provides a theoretical basis for preserving and restoring vegetation.

The refuge effect is critical in ecosystems for stabilizing predator-prey interactions. The purpose of this research was to investigate the complexities of a discrete-time predator-prey system with a refuge effect. The analysis investigated the presence and stability of fixed points, as well as period-doubling and Neimark-Sacker (NS) bifurcations. The bifurcating and fluctuating behavior of the system was controlled via feedback and hybrid control methods. In addition, numerical simulations were performed as evidence to back up our theoretical findings. According to our findings, maintaining an optimal level of refuge availability was critical for predator and prey population cohabitation and stability.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

We investigated synchronization of dynamic systems with mixed delays and delayed impulses. Using impulsive control method and the average impulsive interval approach, several Lyapunov sufficient conditions were given for ensuring synchronization in terms of impulsive perturbation and impulsive control, respectively. The derived conditions indicated that delays in continuous dynamical systems were flexible under impulsive perturbation and were not strictly dependent on the size of impulsive delays, and they may have a potential impact on synchronization of the considered system. In addition, applying the proposed concepts of average positive impulsive estimation and average impulsive estimation, we integrated the information in impulsive delay into the rate coefficient to eliminate the limitation of having the same threshold at each impulse point, while the impulsive delay maintained the synchronization effect. This was an improvement on the previous results obtained. Finally, we provided two numerical examples to illustrate the validity of our results.

This paper investigates the prescribed-time event-triggered cluster practical consensus problem for a class of nonlinear multi-agent systems with external disturbances. To begin, to reach the prescribed-time cluster practical consensus, a new time-varying function is introduced and a novel distributed continuous algorithm is designed. Based on the Lyapunov stability theory and inequality techniques, some sufficient conditions are given, ensuring the prescribed-time cluster practical consensus. Moreover, to avoid different clusters' final states overlapping, a virtual leader is considered for each cluster. In this case, an event-triggered distributed protocol is further established and some related conditions are given for achieving prescribed-time cluster practical consensus. Additionally, it is proven that the Zeno behavior can be avioded by choosing parameters appropriately. Finally, some numerical examples are presented to show the effectiveness of the theoretical results.

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.

Objective: This study evaluates the impact of different combinations of treatment regimens, such as additional radiation, chemotherapy, and surgical treatments, on the survival of elderly rectal cancer patients ≥ 70 years of age to support physicians' clinical decision-making.

Methods: Data from a sample of elderly rectal cancer patients aged ≥ 70 years diagnosed from 2005-2015 from the US surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed. The best cut-off point was selected using the x-tile software for the three continuity indices: age, tumor size, and number of regional lymph nodes. All patients were categorized into either the neoadjuvant radiotherapy and surgery group (R_S group), the surgical treatment group (S group), or the surgery and adjuvant radiotherapy group (S_R group). The propensity score allocation was used to match each included study subject in a 1:1 ratio, and the restricted mean survival time method (RMST) was used to predict the mean survival of rectal cancer patients within 5 and 10 years. The prognostic risk factors for rectal cancer patients were determined using univariate and multivariate Cox regression analyses, and nomograms were constructed. A subgroup stratification analysis of patients with different treatment combination regimens was performed using the Kaplan-Meier method, and log-rank tests were used for between-group comparisons. The model's predictive accuracy was assessed by receiver operating characteristic (ROC) curves, correction curves, and a clinical decision curve analysis (DCA).

Results: A total of 7556 cases of sample data from 2005 to 2015 were included, which were categorized into 6639 patients (87.86%) in the S group, 408 patients (5.4%) in the R_S group, and 509 patients (6.74%) in the S_R group, according to the relevant order of radiotherapy and surgery. After propensity score matching (PSM), the primary clinical characteristics of the groups were balanced and comparable. The difference in the mean survival time before and after PSM was not statistically significant in both R_S and S groups (P value > 0.05), and the difference in the mean survival time after PSM was statistically substantial in S_R and S groups (P value < 0.05). In the multifactorial Cox analysis, the M1 stage and Nodes ≥ 9 were independent risk factors. An age between 70-75 was an independent protective factor for patients with rectal cancer in the R_S and S groups. The Marital_status, T4 stage, N2 stage, M1 stage, and Nodes ≥ 9 were independent risk factors for patients with rectal cancer in the S_R and S groups, and an age between 70-81 was an independent protective factor. The ROC curve area, the model C index, and the survival calibration curve suggested good agreement between the actual and predicted values of the model. The DCA for 3-year, 5-year, and 10-year survival periods indicated that the model had some potentia