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The effects of straw alone or combined with industrial and agricultural wastes as fertilizers on greenhouse gas (GHG) emissions are still poorly known in cropland areas. Here, we studied the effects of 3.5 Mg ha−1 straw and 3.5 Mg ha−1 straw combined with 8 Mg ha−1 of diverse wastes on GHG emission in a subtropical Jasminum sambac plantation in southeastern China. There were five treatments in a completely randomized block design: control, straw only, straw + biochar, straw + steel slag, and straw + gypsum slag. Emissions of carbon dioxide were generally higher in the treatments with waste than in the control or straw-only treatments, whereas the contrary pattern was observed in CH4 and N2O emission rates. Moreover, the total global warming potentials (GWPs) were no significantly higher in most of the amended treatments as compared to the control and straw-only treatments. In relation to the treatment with only straw, GWPs were 9.4% lower when steel slag was used. This finding could be a consequence of Fe amount added by steel slag, which would limit and inhibit the emissions of GHGs and their transport from soil to atmosphere. Our results showed that the application of slags did not increase the emission of GHGs and that the combination of straw with steel slag or biochar could be more effective than straw alone for controlling GHGs emission and improve soil C and nutrient provision.
Many components in diet have regulated oxidative stress, inflammatory reaction and even balance oestrogen levels. Because these factors are closely associated with depressive symptoms in postmenopausal women, it is considered that dietary factors are able to prevent and control depressive symptoms. On the other hand, a dietary pattern that considers the correlations and synergies between foods and nutrients is expected to have a greater impact on disease risk. The aim of the present study is to evaluate whether dietary patterns are associated with depressive symptoms in Chinese postmenopausal women. A cross-sectional study of 2051 postmenopausal women (mean age: 58·8 (sd 7·4) years) was conducted in Tianjin, China. Dietary consumption was assessed by a valid self-administered FFQ. Principal component analysis was used to derive three major dietary patterns: ‘healthy’, ‘sweets’ and ‘traditional Tianjin’ from eighty-eight food items. Depressive symptoms were assessed using the Zung Self-Rating Depression Scale, and cut-off point of 48 indicating serious depressive symptoms. The association between quartile of dietary patterns and depressive symptoms was assessed using multiple logistic regression analysis. The multivariable-adjusted OR of having depressive symptoms for increasing quartile of dietary patterns were as follows: healthy, 1·00, 0·79 (95 % CI 0·49, 1·28), 0·62 (95 % CI 0·37, 1·04) and 0·57 (95 % CI 0·33, 0·97); sweets, 1·00, 0·75 (95 % CI 0·42, 1·3), 1·08 (95 % CI 0·64, 1·81) and 1·66 (95 % CI 1·03, 2·71); and traditional Tianjin, 1·00, 1·02 (95 % CI 0·58, 1·79), 0·96 (95 % CI 0·54, 1·71) and 2·53 (95 % CI 1·58, 4·16), respectively. The present study demonstrated that a healthy dietary pattern was inversely associated with depressive symptoms. On the contrary, greater adherence to sweets and traditional Tianjin dietary patterns was associated with a higher prevalence of depressive symptoms.
Creative Stimulator (CreaStim) is an intelligent interface
for pattern design that behaves as a semiactive partner to human
designers rather than as a passive graphical or computational
tool. By making adjustments to psychological differentials and/or
design parameters, CreaStim is able to help designers to explore
innovative pattern designs and to get inspiration, producing
different types of novel designs. In this article, the mechanism,
the technique, the implementation, and the testing of CreaStim
are described. The basic principle of CreaStim is the catastrophe
theory, which implies that sudden realization in the thinking
process of design may lead to creativity. CreaStim tries to
stimulate and/or impact designers' creativity in design
process using the output of it, rather than to simulate the
sudden realization. The core of the CreaStim is a neural
network-based imagining engine, a data repository, and its learning
strategies considering psychological factors. The psychological
factors, which are thought one of the key influences to creative
design, are based on the questionnaires completed by designers
about the existing successful designs. The repository contains
not only a traditional database storing functional attributes,
economic attributes, graphic description, structural description,
and psychological attributes, but also methods, rule-based
knowledge, and pattern-type knowledge. And it is managed by
an application program called Design Template Group (DTG) manager.
Trained with 12 pieces of successful pattern designs and 528
pieces of pseudo-examples produced and evaluated by the authors,
CreaStim is implemented for a PC and an evaluation poll from
five designers shows that designers may most likely get some
inspiration from the produced patterns and some of them can
even be adopted as the design alternatives directly.
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