Environmentally sustainable development is a multidimensional concept that emphasizes the integration of economy, society and environment within a region and the realization of dynamic balance. How to objectively environmentally sustainable development has been a major concern for scholars and policy makers. To address this problem effectively, we first obtain the indicators of environmentally sustainable development based on the pressure-state-response (PSR) framework. Then, we introduce variable weight factors in the traditional analytic hierarchy process (AHP), so that the weights assigned by experts to sustainable development indicators can change with time or space. In this way, we propose a new and improved weight distribution method called variable weigh analytic hierarchy process. Finally, we employ indicators of environmentally sustainable development based on PSR and variable weigh analytic hierarchy process to evaluate the sustainable development of cities in a case country. Our study found that: (1) indicators of environmentally sustainable development should consist of three parts: pressure indicators of environmentally sustainable development, state indicators of environmentally sustainable development, and response indicators of sustainable development; (2) with the variable weigh analytic hierarchy process, our ranking hierarchy process can handle dynamic changes among indicators better than the traditional AHP method and better reflect the true states of indicators.
Colorectal cancer lymph node metastasis, which is highly associated with the patient's cancer recurrence and survival rate, has been the focus of many therapeutic strategies that are highly associated with the patient's cancer recurrence and survival rate. The popular methods for classification of lymph node metastasis by neural networks, however, show limitations as the available low-level features are inadequate for classification, and the radiologists are unable to quickly review the images. Identifying lymph node metastasis in colorectal cancer is a key factor in the treatment of patients with colorectal cancer. In the present work, an automatic classification method based on deep transfer learning was proposed. Specifically, the method resolved the problem of repetition of low-level features and combined these features with high-level features into a new feature map for classification; and a merged layer which merges all transmitted features from previous layers into a map of the first full connection layer. With a dataset collected from Harbin Medical University Cancer Hospital, the experiment involved a sample of 3,364 patients. Among these samples, 1,646 were positive, and 1,718 were negative. The experiment results showed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.8732, 0.8746, 0.8746 and 0.8728, respectively, and the accuracy and AUC were 0.8358 and 0.8569, respectively. These demonstrated that our method significantly outperformed the previous classification methods for colorectal cancer lymph node metastasis without increasing the depth and width of the model.
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