Estimating the Impacts of Climate Change on Mortality in OECD Countries

The major contribution of this study is to combines both climatic and macroeconomic factors simultaneously in the estimation of mortality using the capital city of 22 OECD countries from the period 1990 to 2008. The empirical results provide strong evidences that higher income and a lower unemployment rate could reduce mortality rates, while the increases in precipitation and temperature variation have significantly positive impacts on the mortality rates. The effects of changing average temperature on mortality rates in summer and winter are asymmetrical and also depend on the location. Combining the future climate change scenarios with the estimation outcomes show that mortality rates in OECD countries in 2100 will be increased by 3.77% to 5.89%.Climate change; mortality; panel data model

Chemotaxonomic Analysis of the Venom Composition within the Ant Genus Strumigenys (Hymenoptera, Formicidae) in Taiwan

In Taiwan, the ant genus Strumigenys is represented by 13 species, nine of which being endemic to this island. Classic morphological taxonomy can be complex and may lead to equivoque identification within this group. To clarify subtle species assignments, we investigated the venom composition of five Strumigenys species, using SPME extraction and GC/MS analyses, and searched for a suitable chemical marker. Our results indicate that three out of the five species tested showed enough specificity in their chemical profiles to allow clear differentiation. However, the two remaining species could not be distinguished from each other on the basis of their venom composition. We further assessed the phylogenetic relationships between the five species, analyzing both morphological and chemical characters. Our clusters revealed congruency between some species associations and suggested that the analysis of venom composition may apply, at least partially, to Strumigenys chemosystematics. However, important discrepancies also appeared, signifying that selective pressures for chemical diversification have operated differentially during the speciation and dispersal processes within this genus inTaiwan

Hidden Trends in 90 Years of Harvard Business Review

In this paper, we demonstrate and discuss results of our mining the abstracts of the publications in Harvard Business Review between 1922 and 2012. Techniques for computing n-grams, collocations, basic sentiment analysis, and named-entity recognition were employed to uncover trends hidden in the abstracts. We present findings about international relationships, sentiment in HBR's abstracts, important international companies, influential technological inventions, renown researchers in management theories, US presidents via chronological analyses.Comment: 6 pages, 14 figures, Proceedings of 2012 International Conference on Technologies and Applications of Artificial Intelligenc

Modeling the Effect of Oil Price on Global Fertilizer Prices

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, the autoregressive distributed lag (ARDL) model, and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, and GJR model, are used to investigate the relationship between crude oil price and six global fertilizer prices. Weekly data for 2003-2008 for the seven price series are analyzed. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price, which explains why global fertilizer prices reached a peak in 2008. We also find that that the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods, and that the peak crude oil price caused greater volatility in the crude oil price and global fertilizer prices. As volatility invokes financial risk, the relationship between oil price and global fertilizer prices and their associated volatility is important for public policy relating to the development of optimal energy use, global agricultural production, and financial integration.Volatility; Global fertilizer price; Crude oil price; Non-renewable fertilizers; Structural breakpoint unit root test

ZigBee Wireless Sensor Nodes with Hybrid Energy Storage System Based on Li-Ion Battery and Solar Energy Supply

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system

ZigBee Wireless Sensor Nodes with Hybrid Energy Storage System Based on Li-Ion Battery and Solar Energy Supply

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system