Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and ...dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, ...delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.
Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such ...as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.
With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have ...conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.
Metro station restoration sequence optimization is crucial during post-disaster recovery. Taking both budget limitations and repair time uncertainty into account, this paper proposes a ...resilience-based optimization model for choosing an optimal restoration sequence scheme, maximizing the global average efficiency, under the condition that the network accessibility meets given resilience requirements. Evolutionary algorithm NSGA-II is applied to solve the model. A Case study in Nanjing and Zhengzhou gives insights into restoration sequence strategies for decision-makers. Results show that a ring network is more robust than a radial network under the same scale attack. Under limited budget, the optimal restoration sequence is closely related to the damaged stations’ location and repair time. Specifically, if damaged stations’ distribution is relatively centralized and transfer stations need more repair time, giving repair priority to transfer stations is not always the best strategy. If damaged stations’ distribution is relatively scattered and all stations’ repair time is the same, the station with a bigger node degree should be repaired earlier. However, this conclusion may be invalid if transfer stations repair time is far longer than others. Sensitivity analysis show that the total budget is more sensitive than one day’s budget in the entire restoration phase. However, in the emergency phase, increasing one day’s budget is more significant for shortening recovery time. The proposed model can contribute to effective and flexible decision-making for metro network restorations.
Medical image segmentation is still a challenging task due to noise and intensity inhomogeneity. An adaptive fuzzy level set model (AFLSM) with local spatial information is presented in this paper ...for accurately segmenting medical images and correcting bias field. A weighting scheme that can ensure each pixel in the neighborhood to have anisotropic weight is first introduced to remove noisy pixels and hence improve the robustness to noise. Then, a linear combination of orthogonal basis functions is used to represent bias field to ensure its smoothly and slowly varying property. Besides, to improve the robustness to initialization, this adaptive fuzzy level set model fuses a level set model with the membership function of fuzzy clustering, which can adaptively adjust the evolution of level set function. Finally, the distance regularization term in energy formulation is redefined with a novel double-well potential function to inherently maintain the accuracy and stability of the AFLSM. The AFLSM is first represented in the two-phase case and subsequently extended to the multi-phase formulation. The numerous visual segmentation results and quantitative evaluation can demonstrate the performance of the AFLSM on synthetic and real medical images. Comparison with the state-of-the-art models shows that the AFLSM can achieve better segmentation results with an improvement of 0.2286 ± 0.1477 in Dice coefficient and 0.1350 ± 0.0661 in Jaccard similarity coefficient in terms of robustness and the capability to correct bias field, respectively.
We used a hydrothermal method to regulate the concentration ratio of surface/bulk oxygen vacancies in CeO2 nanorods. This study provides guidance to develop advanced metal-oxide semiconductor ...photocatalysts for the photodegradation of organic dyes.
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To enhance the photodegradation ability of CeO2 for organic dyes, an effective strategy is to introduce oxygen vacancies (Vo). In general, the introduced Vo are simultaneously present both on the surface and in the bulk of CeO2. The surface oxygen vacancies (Vo-s) can decrease the band gap, thus enhancing light absorption to produce more photogenerated e− for photodegradation. However, the bulk oxygen vacancies (Vo-b) will inhibit photocatalytic activity by increasing the recombination of photogenerated e− and Vo-b. Therefore, regulating the concentrations of Vo-s to Vo-b is a breakthrough for achieving the best utilization of photogenerated e− during photodegradation. We used an easy hydrothermal method to achieve tunable concentrations of Vo-s to Vo-b in CeO2 nanorods. The optimized CeO2 presents a 70.2% removal of rhodamine B after 120 min of ultraviolet−visible light irradiation, and a superior photodegradation performance of multiple organics. This tuning strategy for Vo also provides guidance for developing other advanced metal-oxide semiconductor photocatalysts for the photodegradation of organic dyes.
Most platelet membrane proteins are modified by mucin-type core 1-derived glycans (O-glycans). However, the biological importance of O-glycans in platelet clearance is unclear. Here, we generated ...mice with a hematopoietic cell-specific loss of O-glycans (HC C1galt1
−/−). These mice lack O-glycans on platelets and exhibit reduced peripheral platelet numbers. Platelets from HC C1galt1
−/− mice show reduced levels of α-2,3-linked sialic acids and increased accumulation in the liver relative to wild-type platelets. The preferential accumulation of HC C1galt1
−/− platelets in the liver was reduced in mice lacking the hepatic asialoglycoprotein receptor Ashwell–Morell receptor (AMR). However, we found that Kupffer cells are the primary cells phagocytosing HC C1galt1
−/− platelets in the liver. Our results demonstrate that hepatic AMR promotes preferential adherence to and phagocytosis of desialylated and/or HC C1galt1
−/− platelets by the Kupffer cell through its C-type lectin receptor CLEC4F. These findings provide insights into an essential role for core 1 O-glycosylation of platelets in their clearance in the liver.
Sparse time‐frequency analysis (STFA) can precisely achieve the spectrum of the local truncated signal. However, when the signal is disturbed by unexpected data loss, STFA cannot distinguish ...effective signals from missing data interferences. To address this issue and establish a robust STFA model for time‐frequency analysis (TFA) in data loss scenarios, a stationary Framelet transform‐based morphological component analysis is introduced in the STFA. In the proposed model, the processed signal is regarded as a sum of the cartoon, texture and data‐missing parts. The cartoon and texture parts are reconstructed independently by taking advantage of the stationary Framelet transform. Then, the signal is reconstructed for STFA. The forward‐backwards splitting method is employed to split the robust STFA model into the data recovery and robust time‐frequency imaging stages. The two stages are then solved separately by using the alternating direction method of multipliers (ADMM). Finally, several experiments are conducted to show the performance of the proposed robust STFA method under different data loss levels, and it is compared with some existing state‐of‐the‐art time‐frequency methods. The results indicate that the proposed method outperforms the compared methods in obtaining the sparse spectrum of the effective signal when data are missing. The proposed method has a potential value in TFA in scenarios where data is easily lost.
This study introduces the stationary Framelet transform‐based morphological component analysis in the sparse time‐frequency analysis (STFA) to establish a robust STFA model for data loss scenarios to address the issue of STFA's failure to distinguish effective signals from data missing interferences when the signal is disturbed by unexpected data loss.
In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue ...accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.
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