Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

Fluorescent chemodosimeters using “mild” chemical events for the detection of small anions and cations in biological and environmental media

Abstract: Mild chemical processes of various analytes and detection methods involving revolutionary strategies in the fields of analytical chemistry, biology and environmental sciences have been extensively developed. This critical review focuses on representative examples of mild chemical processes that can be used in fluorescent chemodosimeters for ion sensing (anions and cations). A systematisation according to the type of reaction mechanism is established. Numerous examples including extensions combined with catalytic and material sciences applicable in fluorescence imaging and water treatment are also discussed (151 references).

Progressive Attention Guided Recurrent Network for Salient Object Detection

Abstract: Effective convolutional features play an important role in saliency estimation but how to learn powerful features for saliency is still a challenging task. FCN-based methods directly apply multi-level convolutional features without distinction, which leads to sub-optimal results due to the distraction from redundant details. In this paper, we propose a novel attention guided network which selectively integrates multi-level contextual information in a progressive manner. Attentive features generated by our network can alleviate distraction of background thus achieve better performance. On the other hand, it is observed that most of existing algorithms conduct salient object detection by exploiting side-output features of the backbone feature extraction network. However, shallower layers of backbone network lack the ability to obtain global semantic information, which limits the effective feature learning. To address the problem, we introduce multi-path recurrent feedback to enhance our proposed progressive attention driven framework. Through multi-path recurrent connections, global semantic information from the top convolutional layer is transferred to shallower layers, which intrinsically refines the entire network. Experimental results on six benchmark datasets demonstrate that our algorithm performs favorably against the state-of-the-art approaches.

Green supply chain management implications for “closing the loop”

Abstract: In this paper we report on results from a cross-sectional survey with manufacturers in four typical Chinese industries, i.e., power generating, chemical/petroleum, electrical/electronic and automobile, to evaluate their perceived green supply chain management (GSCM) practices and relate them to closing the supply chain loop. Our findings provide insights into the capabilities of Chinese organizations on the adoption of GSCM practices in different industrial contexts and that these practices are not considered equitably across the four industries. Academic and managerial implications of our findings are discussed.

Saliency Detection via Absorbing Markov Chain

Abstract: In this paper, we formulate saliency detection via absorbing Markov chain on an image graph model. We jointly consider the appearance divergence and spatial distribution of salient objects and the background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The absorbed time of transient node measures its global similarity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth background regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the proposed method against the state-of-the-art methods.

Crustal deformation of the eastern Tibetan plateau revealed by magnetotelluric imaging

Abstract: The ongoing collision of the Indian and Asian continents has created the Himalaya and Tibetan plateau through a range of deformation processes. These include crustal thickening, detachment of the lower lithosphere from the plate (delamination) and flow in a weakened lower crust 1‐6 . Debate continues as to which of these processes are most significant 7 . In eastern Tibet, large-scale motion of the surface occurs, but the nature of deformation at depth remains unresolved. A large-scale crustal flow channel has been proposed as an explanation for regional uplift in eastern Tibet 6,8,9 , but existing geophysical data 10,11 do not constrain the pattern of flow. Magnetotellurics uses naturally occurring electromagnetic waves to image the Earth’s subsurface. Here we present magnetotelluric data that image two major zones or channels of high electrical conductivity at a depth of 20-40 km. The channels extend horizontally more than 800 km from the Tibetan plateau into southwest China. The electrical properties of the channels imply an elevated fluid content consistent with a weak crust 12,13 that permits flow on a geological timescale. These findings support the hypothesis that crustal flow can occur in orogenic belts and contribute to uplift of plateaux. Our results reveal the previously unknown complexities of these patterns of crustal flow. Many previous studies of the IndiaAsia