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Seagrass systems of the Western Pacific region are biodiverse habitats, providing vital services to ecosystems and humans over a vast geographic range. SeagrassNet is a worldwide monitoring program that collects data on seagrass habitats, including the ten locations across the Western Pacific reported here where change at various scales was rapidly detected. Three sites remote from human influence were stable. Seagrasses declined largely due to increased nutrient loading (4 sites) and increased sedimentation (3 sites), the two most common stressors of seagrass worldwide. Two sites experienced near-total loss from of excess sedimentation, followed by partial recovery once sedimentation was reduced. Species shifts were observed at every site with recovering sites colonized by pioneer species. Regulation of watersheds is essential if marine protected areas are to preserve seagrass meadows. Seagrasses in the Western Pacific experience stress due to human impacts despite the vastness of the ocean area and low development pressures.

Abstract This paper presents a novel multi-time-stage input–output-based modeling framework for simulating the dynamics of bioenergy supply chains. One of the key assumptions used in the model is that the production level at the next time-stage of each segment of the energy supply chain adjusts to the output surplus or deficit relative to targets at the current time period. Furthermore, unlike conventional input–output models, the technology matrix in this approach need not be square, and thus can include coefficients denoting flows of environmental goods, such as natural resources or pollutants. Introducing a feedback control term enables the system to regulate the dynamics, thus extending the model further. This is an important feature since the uncontrolled dynamic model exhibits oscillatory or unstable behavior under some conditions; in principle, the control term allows such undesirable characteristics to be suppressed. Numerical simulations of a simple, two-sector case study are given to illustrate dynamic behavior under different scenarios. Although the case study uses only a hypothetical system, preliminary comparisons are made between the simulation results and some broad trends seen in real bioenergy systems. Finally, some of the main policy implications of the model are discussed based on the general dynamic characteristics seen in the case study. In particular, insights from control theory can be used to develop policy interventions to impart desirable dynamic characteristics to nascent or emerging biofuel supply chains. These interventions can be used to guide the growth of bioenergy supplies along final demand trajectories with minimal fluctuation and no instability.

Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (approximately 100-150 g C m(-2) yr(-1)) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be approximately 28 Pg and approximately 46 Pg C/yr, respectively. The NCEAS model estimated an approximately 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.

Abstract In this communication, a mathematical model is presented to predict the thermal performance of a spherical solar still. The analysis is based on the heat and mass transfer relationships by Dunkle (1961) which have been modified empirically so as to validate the experimental results of Menguy et al. (1980). Numerical calculations have also been made to study the effect of the absorptivity of the basin liner on the distillate yield of the still

Abstract International politics affects the oil trade. But do financial and commercial traders who participate in spot oil trading also respond to changes in international politics? We construct a firm-level dataset for all U.S. oil-importing companies over 1986–2008 to examine how these firms respond to increases in “political distance” between the U.S. and her trading partners, measured by divergence in their UN General Assembly voting patterns. Consistent with previous macro evidence, we first show that individual firms diversify their oil imports politically, even after controlling for unobserved firm heterogeneity. However, the political pattern of oil imports is not entirely driven by the concerns of hold-up risks, which exist when oil transactions via term contracts are associated with backward vertical FDI that is subject to expropriation. In particular, our results indicate that even financial and commercial traders significantly reduce their oil imports from U.S. political enemies. Interestingly, while these traders diversify their oil imports politically immediately after changes in international politics, other oil companies reduce their oil imports with a significant time lag. Our findings suggest that in designing regulations to avoid harmful repercussions on commodity and financial assets, policymakers need to understand the nature of political risk.

AbstractThe presented synthesis of the title compounds (II), (IV), (VI) and (VIII) is extremely simple and highly efficient without the use of any chromatographic purification.

Abstract The effects of hCG (CR119) and ethanol (20% in saline, v/v) on the levels of gonadotropin receptor in mature rat testes are studied. The results indicate that 75 I.U. of hCG administered intraperitoneally is capable of depleting testicular tissue of all its gonadotropin binding sites within 24 hrs. after administration, and 11–14 days are required for these binding sites to be fully restored. The disappearance and reappearance patterns of testicular gonadotropin binding sites following in vivo ethanol administration are very similar to those seen after gonadotropin administration, the only difference being that ethanol is much less immediate in its effect. The level of hLH in the serum of these rats is unaffected by the ethanol administered which seems to indicate that reduction of the number of gonadotropin binding sites in testes brought about by ethanol is through membrane-mediated mechanism. For rats receiving gonadotropin injections, no correlation sseems to exist between the concentration of gonadotropin in serum and the level of detectable gonadotropin binding sites in the testis.

Electricity price forecasting is critical to numerous tasks in the power system such as strategic bidding, generation scheduling, optimal scheduling of storage reserves and system analysis. Most existing price forecasting models focus on hourly prediction for the day ahead market. This work focuses on the real-time, 5-minute market, with the goal of developing a model able to capture both the long- and short-term temporal distribution of the data. Extending the recent advances in deep learning models of time series forecasting, the proposed model - named HFnet - is a novel multi-branch Gated Recurrent Unit (GRU) architecture for electricity price forecasting. Extensive empirical analyses using real-time data from the New York Independent System Operator (NYISO) illustrate the value of the proposed model when compared to state-of-art prediction models, with an average reduction in error of 10%.

Mixed transition metal oxides with high theoretical capacity show great potential to replace carbonaceous anode materials in lithium-ion batteries.