Circular economy model for recycling waste resources under government participation: a case study in industrial waste water circulation in China

A circular economy (CE) is proposed to mitigate resource shortage and environmental pollution. Given the inevitable conflict between the new development mode and traditional economic benefits, practical experience shows that CE implementation needs the support of outside forces, such as government policy interventions or environmental organisations’ propaganda guidance. On the basis of existing studies, the present work establishes a systematic economic model in accordance with the characteristics, objectives and principles of CE. The equilibrium solution and critical condition of government and non-government participation models are obtained through game analysis. We select the industrial waste water circulation of some provinces in China to illustrate the feasibility and effectiveness of the proposed model. Overall, our results indicate that the gap between the critical price and unit processing cost determines the promotion of CE and government intervention. Moreover, government intervention is critical to building a waste management department in its early stage. First published online 27 November 201

Surgical treatment strategy for multiple injury patients in ICU

AbstractObjectiveTo investigate the surgical treatment for patients with multiple injuries in ICU.MethodsClinical data of 163 multiple injury patients admitted to ICU of our hospital from January 2006 to January 2009 were retrospectively studied, including 118 males and 45 females, with the mean age of 36.2 years (range, 5-67 years). The injury regions included head and neck (29 cases), face (32 cases), chest (89 cases), abdomen (77 cases), pelvis and limbs (91 cases) and body surface (83 cases). There were 57 cases combined with shock. ISS values varied from 10 to 54, 18.42 on average. Patients received surgical treatments in ICU within respectively 24 hours (10 cases), 24-48 hours (8 cases), 3-7 days (7 cases) and 8-14 days (23 cases).ResultsFor the 163 patients, the duration of ICU stay ranged from 2 to 29 days, with the average value of 7.56 days. Among them, 143 were cured (87.73%), 11 died in the hospital (6.75%) due to severe hemorrhagic shock (6 cases), craniocerebral injury (3 cases) and multiple organ failure (2 cases), and 9 died after voluntarily discharging from hospital (5.52%). The total mortality rate was 12.27%.ConclusionsThe damage control principle should be followed when multiple injury patients are resuscitated in ICU. Surgical treatment strategies include actively controlling hemorrhage, treating the previously missed injuries and related wounds or surgical complications and performing planned staging operations

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

Chemical ordering suppresses large-scale electronic phase separation in doped manganites

For strongly correlated oxides, it has been a long-standing issue regarding the role of the chemical ordering of the dopants on the physical properties. Here, using unit cell by unit cell superlattice growth technique, we determine the role of chemical ordering of the Pr dopant in a colossal magnetoresistant (La1-yPry)1-xCaxMnO3 (LPCMO) system, which has been well known for its large length-scale electronic phase separation phenomena. Our experimental results show that the chemical ordering of Pr leads to marked reduction of the length scale of electronic phase separations. Moreover, compared with the conventional Pr-disordered LPCMO system, the Pr-ordered LPCMO system has a metal–insulator transition that is ~100 K higher because the ferromagnetic metallic phase is more dominant at all temperatures below the Curie temperature

Neural Dependencies Emerging from Learning Massive Categories

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call \textbf{neural dependency}. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code for this work will be made publicly available