Effects of fully open-air CO2 elevation on leaf ultrastructure, photosynthesis and yield of two soybean cultivars.

The objective of this study was to investigate the effect of elevated (550 ± 17 μmol mol−1) CO2 concentration ([CO2]) on leaf ultrastructure, leaf photosynthesis and seed yield of two soybean cultivars [Glycine max (L.) Merr. cv. Zhonghuang 13 and cv. Zhonghuang 35] at the Free-Air Carbon dioxide Enrichment (FACE) experimental facility in North China. Photosynthetic acclimation occurred in soybean plants exposed to long-term elevated [CO2] and varied with cultivars and developmental stages. Photosynthetic acclimation occurred at the beginning bloom (R1) stage for both cultivars, but at the beginning seed (R5) stage only for Zhonghuang 13. No photosynthetic acclimation occurred at the beginning pod (R3) stage for either cultivar. Elevated [CO2] increased the number and size of starch grains in chloroplasts of the two cultivars. Soybean leaf senescence was accelerated under elevated [CO2], determined by unclear chloroplast membrane and blurred grana layer at the beginning bloom (R1) stage. The different photosynthesis response to elevated [CO2] between cultivars at the beginning seed (R5) contributed to the yield difference under elevated [CO2]. Elevated [CO2] significantly increased the yield of Zhonghuang 35 by 26% with the increased pod number of 31%, but not for Zhonghuang 13 without changes of pod number. We conclude that the occurrence of photosynthetic acclimation at the beginning seed (R5) stage for Zhonghuang 13 restricted the development of extra C sink under elevated [CO2], thereby limiting the response to elevated [CO2] for the seed yield of this cultivar.

Artificial Intelligence: the basics

Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined, whether machines can 'think', sensory input in machine systems, the nature of consciousness, the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.

A model of UK domestic appliance-use using Artificial Neural Networks

The development of an Artificial Neural Network model of UK domestic appliance energy consumption is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 households during the summer of 2010. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with backpropagation training and has a12:10:24architecture.Model outputs include appliance load profiles which can be applied to the fields of energy planning (micro renewables and smart grids), building simulation tools and energy policy.

Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational.

The link between temporal attention and emotion: a playground for psychology, neuroscience, and plausible artificial neural networks

In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion.