How to Enhance Citizens' Sense of Gain in Smart Cities? A SWOT-AHP-TOWS Approach.

The previous technology-centric development of smart cities mainly focuses on the numbers, diversities, and types of applied intelligent technologies, while the citizen-centric smart city has become an important paradigm for improving the sustainability of cities around the world. The citizens' sense of gain (CSG), which considers both material acquisition and spiritual feelings of smart city services, is thus proposed and regarded as one of the core orientations in the smart cities' transformation development process from the centric of advanced technology applied to the centric of citizen subjective perception. To shift smart cities from being technology-centric to citizen-centric, it is critical to identify the factors influencing CSG and develop appropriate strategies to enhance CSG in smart cities. Hence, this work identifies 17 key CSG influencing factors based on the dimensions dissected from the definition of CSG and it further formulates 15 strategies for enhancing CSG by adopting the SWOT-AHP-TOWS method based on data collected from Nanjing citizens. The results indicate that the most important criteria for enhancing CSG in smart cities are the external opportunities, which are originated from citizens' attitudes and behaviors, and the top-ranked strategy is "dividing smart infrastructure into different categories according to the hierarchy needs of citizens and promoting the synergy development of smart infrastructure within and among different categories". Finally, four implications are proposed, including (i) strengthening publicity and encouraging citizen participation, (ii) clarifying the responsibilities of local governments, (iii) prioritizing citizens' needs, and (iv) promoting age-friendly, vulnerable-friendly, and environmental-friendly development.

A hybrid method for evaluating the resilience of urban road traffic network under flood disaster: An example of Nanjing, China.

Urban road traffic network (URTN) plays an important role in city operation, while it is also suffered a lot from the urban flood disasters which caused negative impacts frequently, like traffic congestion, and road collapse. The function loss of URTN not only destroy normal urban life and work order, but also pose a serious threat to people's lives and properties. Therefore, it is urgent to quantitatively explore the flood resilience of URTN. The concept of resilience puts forward new ideas to help solve the problem of urban flooding disasters from a holistic view. Exploring the flood resilience of urban traffic network may help to mitigate urban flooding and improve the urban resilience. This paper developed a flood resilience evaluation model of URTN, which contains 26 indicators based on the 4R theory. A case study was conducted in southern China to validate the model with real data. It evaluated the urban flood resilience of road traffic network with a comparison of before and after reconstruction of the pipeline. The results demonstrated that the flood resilience of URTN is at a relatively low level in the study area, and the limitation of single traditional engineering measure to the flood resilience of URTN. Suggestions such as strengthening the citizen participation and enhancing the complementary capability of multiple engineering measures are proposed to further promote the flood resilience of the URTN.

Automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying solid breast masses.

This study aimed to evaluate the performance of automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying benign and malignant breast masses. This retrospective study included 141 ultrasound elastographic studies (93 benign and 48 malignant masses). A novel computer-assisted system was developed for the automatic segmentation of the targeted lesion from cine-loops of real-time sonoelastography. Its hard ratio, defined as the ratio of the number of hard pixels within the tumor divided by the total number of pixels of the whole tumor, was also calculated. The targeted mass was segmented by edge-detection and region growing methods, with combined motion registration after manually defining the original seed. Signal-to-noise ratio (SNR(e)) and contrast-to-noise ratio (CNR(e)) of ultrasound elastogram were computed to obtain an optimum slice for differentiating benign and malignant lesions. The diagnostic results of automatic slice selection using maximum strain, maximum SNR(e), maximum CNR(e), maximum compression and the slices selected by radiologists were compared. Mann-Whitney U test, performance indexes and receiver operating characteristic (ROC) curves were used for statistical analysis. Performance using the maximum SNR(e) (accuracy 84.4%, sensitivity 83.3%, specificity 85.0% and A(z) value 0.90) was the best as compared with those of maximum CNR(e) (82.3%, 79.2%, 83.9% and 0.88, respectively), maximum compression (78.0%, 79.2%, 77.4% and 0.85, respectively), maximum strain (79.4%, 79.2%, 79.6% and 0.87, respectively) and radiologists' selection (77.3%, 77.1%, 77.4% and 0.80, respectively). Automatic selection of representative slice from the cine-loops of real-time sonoelastography is a practical, objective and accurate approach for classifying solid breast masses.