Prediction and analysis of the residual capacity of concrete-filled steel tube stub columns under axial compression subjected to combined freeze-thaw cycles and acid rain corrosion

© 2019 by the authors. This paper presents a theoretical investigation on the safety evaluation, stability evaluation, and service life prediction of concrete-filled steel tube (CFST) structures in a Northern China area with acid rain. The finite element software ABAQUS was used to establish a numerical model, which was used to simulate the axial compression behavior of CFST columns subjected to the combined actions of freeze-thaw cycles and acid rain corrosion. The model performance was validated using the experimental results of the evaluation of mechanical properties, including the failure mode and load-displacement curve. Then, the effects of the section size, material strength, steel ratio, and combined times on the residual capacity were studied. The results show that the section size has a smaller influence on the residual strength than the other parameters and can be neglected in the design procedure. However, the other parameters, including the material strength, steel ratio, and combined times have relatively large influences on the axial compressive performance of CFST stub columns subjected to a combination of freeze-thaw cycles and acid rain corrosion. Finally, design formulae for predicting the residual strength of CFST stub columns that are under axial compression and the combined effect of freeze-thaw cycles and acid rain corrosion are proposed, and their results agree well with the numerical results

A Novel Local Community Detection Method Using Evolutionary Computation.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained

Bidirectional multi-scale attention networks for semantic segmentation of oblique UAV imagery

Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation. Most of the semantic segmentation research focused on scenes captured in nadir view, in which objects have relatively smaller scale variation compared with scenes captured in oblique view. The huge scale variation of objects in oblique images limits the performance of deep neural networks (DNN) that process images in a single scale fashion. In order to tackle the scale variation issue, in this paper, we propose the novel bidirectional multi-scale attention networks, which fuse features from multiple scales bidirectionally for more adaptive and effective feature extraction. The experiments are conducted on the UAVid2020 dataset and have shown the effectiveness of our method. Our model achieved the state-of-the-art (SOTA) result with a mean intersection over union (mIoU) score of 70.80%

Marginalized average attentional network for weakly-supervised learning

© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O(2T) to O(T2). Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization

Investigating users’ perspectives on the development of bike-sharing in Shanghai

High levels of car dependence have caused tremendous challenges for sustainable transport development. Transport planners, therefore, seek ways of replacing motor vehicles, as well as increasing the proportion of active travel. The bike-sharing scheme can be seen as an effective way of doing so, particularly in Asian cities. The aim of this paper is to investigate users’ perspectives on the development of bike-sharing using Shanghai as an example. Semi-structured interviews are used to examine the main factors motivating and impeding the development of the bike-sharing scheme in Shanghai. Our findings show that convenience, saving time and financial savings are the major motivations; whereas problems with bicycles being poorly maintained and abused by users, operational issues, financial issues and an unsuitable business model are the major obstacles. In addition, the findings also suggest that a public and private partnership could be the best option for running a sustainable bike-sharing scheme with clear areas of responsibility. Financial incentives, a bicycle-friendly infrastructure, regular operational management and supportive policies should be prioritised. In order to achieve the targets set by the Shanghai Master Plan 2035, transport planners and policymakers should integrate the bike-sharing scheme within the wider active travel system

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation

Effect of nonnutritive sucking and oral stimulation on feeding performance in preterm infants: a randomized controlled trial.

OBJECTIVES: To evaluate the effectiveness of nonnutritive sucking (NNS) and oral stimulation (OS), either applied alone or in combination, to reduce the transition time from tube feeding to independent oral feeding. DESIGN: Randomized controlled trial. SETTING: A 40-bed neonatal ICU in a university hospital in the People's Republic of China. PATIENTS: A total of 120 preterm infants were admitted to the neonatal ICU from December 2012 to July 2013. INTERVENTIONS: Oral motor interventions. MEASUREMENTS AND MAIN RESULTS: One hundred twelve preterm infants were assigned to three intervention groups (NNS, OS, and combined NNS + OS) and one control group. Primary outcome was the number of days needed from introduction of oral feeding to autonomous oral feeding (transition time). Secondary outcome measures were the rate of milk transfer (mL/min), proficiency (intake first 5 min/volume ordered), volume transfer (volume transferred during entire feeding/volume prescribed), weight, and hospital length of stay. Transition time was reduced in the three intervention groups compared with the control group (p < 0.001). The milk transfer rate in the three intervention groups was greater than in the control group (F3,363 = 15.37; p < 0.001). Proficiency in the NNS and OS groups did not exceed that in the control group while the proficiency in the NNS + OS group was greater than that in the control group at the stage when the infants initiated the oral feeding (p = 0.035). Among all groups, no significant difference was found on weight gain and length of stay. CONCLUSIONS: The combined NNS + OS intervention reduced the transition time from introduction to independent oral feeding and enhanced the milk transfer rate. The combined intervention seems to have a beneficial effect on oral feeding proficiency in preterm infants

Spatial patterns and drivers of angiosperm sexual systems in China differ between woody and herbaceous species

Plant sexual systems play an important role in the evolution of angiosperm diversity. However, large-scale patterns in the frequencies of sexual systems (i.e. dioecy, monoecy, and hermaphroditism) and their drivers for species with different growth forms remain poorly known. Here, using a newly compiled database on the sexual systems and distributions of 19780 angiosperm species in China, we map the large-scale geographical patterns in frequencies of the sexual systems of woody and herbaceous species separately. We use these data to test the following two hypotheses: (1) the prevalence of sexual systems differs between woody and herbaceous assemblies because woody plants have taller canopies and are found in warm and humid climates; (2) the relative contributions of different drivers (specifically climate, evolutionary age, and mature plant height) to these patterns differ between woody and herbaceous species. We show that geographical patterns in proportions of different sexual systems (especially dioecy) differ between woody and herbaceous species. Geographical variations in sexual systems of woody species were influenced by climate, evolutionary age and plant height. In contrast, these have only weakly significant effects on the patterns of sexual systems of herbaceous species. We suggest that differences between species with woody and herbaceous growth forms in terms of biogeographic patterns of sexual systems, and their drivers, may reflect their differences in physiological and ecological adaptions, as well as the coevolution of sexual system with vegetative traits in response to environmental changes

Designing and Optimizing a Healthcare Kiosk for the Community

Investigating new ways to deliver care, such as the use of self-service kiosks to collect and monitor signs of wellness, supports healthcare efficiency and inclusivity. Self-service kiosks offer this potential, but there is a need for solutions to meet acceptable standards, e.g., provision of accurate measurements. This study investigates the design and optimization of a prototype healthcare kiosk to collect vital signs measures. The design problem was decomposed, formalized, focused and used to generate multiple solutions. Systematic implementation and evaluation allowed for the optimization of measurement accuracy, first for individuals and then for a population. The optimized solution was tested independently to check the suitability of the methods, and quality of the solution. The process resulted in a reduction of measurement noise and an optimal fit, in terms of the positioning of measurement devices. This guaranteed the accuracy of the solution and provides a general methodology for similar design problems