Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS

Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures

Investigating esophageal sarcomatoid carcinoma and its comparison with esophageal squamous cell carcinoma on clinicopathological characteristics, prognosis, and radiomics features: a retrospective study

IntroductionEsophageal sarcomatoid carcinoma (ESC) is a rare pathological subtype of esophageal carcinomas, wherein its epithelial component typically demonstrates squamous cell carcinoma (SCC). However, the clinicopathological features and prognosis of ESC remain unclear, alongside its unique aspects compared to esophageal SCC (ESCC).MethodsBetween January 2008 and December 2018, we retrospectively reviewed 67 ESC patients treated at West China Hospital. Among them, 51 patients with resected ESC were matched with 98 resected ESCC patients over the same period using propensity score matching at 1:2. The survival time and radiomics features of the two groups were compared.ResultsA total of 59 patients with resected ESC and eight patients with non-resected ESC were enrolled. Progression-free survival (PFS) and overall survival (OS) were significantly different in patients with different TNM stages (p < 0.001). A multivariate analysis showed that length of tumor was an independent factor for OS in resetable ESC (p = 0.041). Among matched ESC and ESCC patients, OS was significantly longer for patients with ESC than those with ESCC (5-year OS, 61.1% vs. 43.6%; HR 0.59, 95% CI 0.35–0.96; p = 0.032). A Rad-score for discriminating ESC from ESCC containing two CT-derived radiomics features was developed [area under the curve: 0.823 (95% CI 0.732–0.913) in the training cohort and 0.828 (95% CI 0.636–1.000) in the validation cohort, respectively].ConclusionsESC has a better prognosis when compared with ESCC. By developing a radiomics prediction model, we provide reliability and convenience for the differential diagnosis of ESC from ESCC

Numerical simulation of chloride diffusion in cementitious materials by lattice type model

The chloride ingress is one of the most significant problems to reinforced concrete structures in coastal areas and cold regions where the de-icing salt is commonly used. In this paper, the lattice type model which has been widely used in fracture analysis of brittle materials is applied to simulate the chloride diffusion process in cementitious materials. The theoretical background of the lattice type model in solving the mass transport problem is briefly presented. The analytical solution of the Fick’s law is adopted to theoretically validate the developed lattice type model. After that, two typical case studies are included to demonstrate the application of the lattice type model in the chloride ingress issue. In the first case, the tortuosity effect of the aggregates on the chloride diffusion front at meso-scale is studied by the lattice model. In the second case, the lattice model is applied in the simulation of the chloride diffusion in cracked concrete. The results show that the lattice type model can be a useful tool to simulate the chloride ingress in the cementitious materials