Abaxial Anthocyanin Layer in Leaves of Tropical Rain Forest Plants: Enhancer of Light Capture in Deep Shade

The permanent pigmentation of the leaves of tropical rain forest herbs with anthocyanin has traditionally been viewed as a mechanism for enhancing transpiration by increased heat absorption. We report measurements to ?+0.1?0C on four Indo-mal- esian forest species polymorphic with respect to color. There were no detectable differences in temperature between cyanic and green leaves. In deeply shaded habitats, any temperature difference would arise from black-body infrared radiation which all leaves absorb and to which anthocyanins are transparent. Reflectance spectra of the lower leaf surfaces of these species re- vealed increased reflectance around 650-750 nm for cyanic leaves compared with green leaves of the same species. In all spe- cies anthocyanin was located in a single layer of cells immediately below the photosynthetic tissue. These observations provide empirical evidence that the cyanic layer can improve photosynthetic energy capture by back-scattering additional light through the photosynthetic tissue.

Clean hydrogen production and carbon dioxide capture methods

Fossil fuels constitute a significant fraction of the world's energy demand. The burning of fossil fuels emits huge amounts of carbon dioxide into the atmosphere. Therefore, the limited availability of fossil fuel resources and the environmental impact of their use require a change to alternative energy sources or carriers (such as hydrogen) in the foreseeable future. The development of methods to mitigate carbon dioxide emission into the atmosphere is equally important. Hence, extensive research has been carried out on the development of cost-effective technologies for carbon dioxide capture and techniques to establish hydrogen economy. Hydrogen is a clean energy fuel with a very high specific energy content of about 120MJ/kg and an energy density of 10Wh/kg. However, its potential is limited by the lack of environment-friendly production methods and a suitable storage medium. Conventional hydrogen production methods such as Steam-methane-reformation and Coal-gasification were modified by the inclusion of NaOH. The modified methods are thermodynamically more favorable and can be regarded as near-zero emission production routes. Further, suitable catalysts were employed to accelerate the proposed NaOH-assisted reactions and a relation between reaction yield and catalyst size has been established. A 1:1:1 molar mixture of LiAlH 4, NaNH2 and MgH2 were investigated as a potential hydrogen storage medium. The hydrogen desorption mechanism was explored using in-situ XRD and Raman Spectroscopy. Mesoporous metal oxides were assessed for CO2 capture at both power and non-power sectors. A 96.96% of mesoporous MgO (325 mesh size, surface area = 95.08 ± 1.5 m2/g) was converted to MgCO 3 at 350°C and 10 bars CO2. But the absorption capacity of 1h ball milled zinc oxide was low, 0.198 gCO2 /gZnO at 75°C and 10 bars CO2. Interestingly, 57% mass conversion of Fe and Fe 3O4 mixture to FeCO3 was observed at 200°C and 10 bars CO2. MgO, ZnO and Fe3O4 could be completely regenerated at 550°C, 250°C and 350°C respectively. Furthermore, the possible retrofit of MgO and a mixture of Fe and Fe3O 4 to a 300 MWe coal-fired power plant and iron making industry were also evaluated.

Public perception of carbon capture and storage in relation to hydraulic fracturing: content analyses, survey results, and policy proposals

Carbon capture and storage (CCS) can contribute significantly to addressing the global greenhouse gas (GHG) emissions problem. Despite widespread political support, CCS remains unknown to the general public. Public perception researchers have found that, when asked, the public is relatively unfamiliar with CCS yet many individuals voice specific safety concerns regarding the technology. We believe this leads many stakeholders conflate CCS with the better-known and more visible technology hydraulic fracturing (fracking). We support this with content analysis of media coverage, web analytics, and public lobbying records. Furthermore, we present results from a survey of United States residents. This first-of-its-kind survey assessed participants’ knowledge, opinions and support of CCS and fracking technologies. The survey showed that participants had more knowledge of fracking than CCS, and that knowledge of fracking made participants less willing to support CCS projects. Additionally, it showed that participants viewed the two technologies as having similar risks and similar risk intensities. In the CCS stakeholder literature, judgment and decision-making (JDM) frameworks are noticeably absent, and public perception is not discussed using any cognitive biases as a way of understanding or explaining irrational decisions, yet these survey results show evidence of both anchoring bias and the ambiguity effect. Public acceptance of CCS is essential for a national low-carbon future plan. In conclusion, we propose changes in communications and incentives as programs to increase support of CCS.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.