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The Indian Ocean Experiment (INDOEX) was an international, multiplatform field campaign to measure long-range transport of air pollution from South and Southeast Asia toward the Indian Ocean during the dry monsoon season in January to March 1999. Surprisingly high pollution levels were observed over the entire northern Indian Ocean toward the Intertropical Convergence Zone at about 6°S. We show that agricultural burning and especially biofuel use enhance carbon monoxide concentrations. Fossil fuel combustion and biomass burning cause a high aerosol loading. The growing pollution in this region gives rise to extensive air quality degradation with local, regional, and global implications, including a reduction of the oxidizing power of the atmosphere.

Species distribution models (SDMs) provide realistic scenarios to explain the influence of bioclimatic variables on plant pathogen distribution. Diplodia sapinea is most harmful to plantations of both exotic and native pine species in Italy, causing economic consequences expecially to edible seed production. In this study, we developed maximum entropy models for D. sapinea in Italy to reach the following goals: (i) to carry out the pathogen's first geographical distribution analysis in Italy and determine which ecogeographical variables (EGVs) may influence its outbreaks; (ii) to detect the effect of climate change on the potential occurrence of disease outbreaks by 2050 and 2070. We used Maxent ver. 3.4.0 to develop SDMs. We used six global climate models (BCC-CSM1-1, CCSM4, GISS-E2-R, MIROC5, HadGEM2-ES and MPI-ESM-LR) for two representative concentration pathways (4.5 and 8.5) and two time projections (2050 and 2070) to detect future climate projections of D. sapinea. The most important EGVs influencing outbreaks were land cover, altitude, mean temperature of driest and wettest quarter, precipitation of wettest quarter, precipitation seasonality and minimum temperature of coldest month. The distribution of D. sapinea mostly expanded in central and southern Italy and shifted in altitude upwards on average by ca. 93m a.s.l. Moreover the fungus expanded the range where disease outbreaks may be recorded in response to an increase in the mean temperature of wettest and driest quarter by ca. 1.9 C and 5.8 C, respectively in all climate change scenarios. Precipitation of wettest quarter did not differ between current and any of future models. Under different climate change scenarios D. sapinea's disease outbreaks will be likely to affect larger areas of pine forests in the country, probably causing heavy effects on the dynamics and evolution of these stands or perhaps constraining their survival.

Roundtables for sustainable beef have evolved in national contexts as well as at the global level as a multi-stakeholder process to address sustainability concerns in the cattle sector. However, due to their relatively recent inception, the literature on the beef roundtables is extremely limited and very little scholarly work has traced their process or impact. We used semi-structured interviews with key informants to examine the governance, actions, and potential impacts of the roundtables for sustainable beef, and identified opportunities and challenges for achieving greater sustainability impact. We found that the beef roundtables are in different stages of development and implementation and that they have diverse approaches based on their geographic contexts. However, they have universally adopted a model of sector-wide continuous improvement, in contrast to roundtables for other commodities, which have in many cases adopted formal certification programs. Activities by the roundtables for sustainable beef have variously included working towards definitions of sustainable beef; setting sustainability principles and criteria; and creating working groups to address specific aspects of sustainability (e.g., verification, deforestation). Our interviews identified opportunities to expand the roundtables’ roles, activities, and sustainability impacts. This study provides a benchmark of the roundtables’ efforts to date, and generates hypotheses and ideas for how they could evolve in the future.

The hollow-fibre oxygenator is a key component of any extracorporeal circuit used to provide cardiopulmonary bypass (CPB) during open-heart surgery. Since the oxygenator is placed downstream of the pump, the energy losses over it have a direct impact on the quality of pulsatile pressure and flow waveforms. The objective of this study was to describe the effects of hydrodynamic characteristics of the oxygenator on energy transfer during pulsatile, normothermic CPB. Twenty-three adult patients scheduled for coronary bypass surgery were divided randomly into two groups, using either an oxygenator (Group 1) with a relatively high-resistance and low-compliance (2079 ± 148 dyn.s.cm-5 and 0.00348 ± 0.00071 ml.mmHg-1, respectively) or an oxygenator (Group 2) with a relatively low-resistance and high-compliance (884 ± 464 dyn.s.cm-5 and 0.01325 ± 0.00161 ml .mmHg-1, respectively). During perfusion, pre- and post-oxygenator pressures, radial artery pressure, and blood flow were recorded simultaneously. A 32% decline of mean pressure was observed in Group 1 and a 16% decline in Group 2 (p<0.0001). Another decrease by approximately 73% in mean pressure in the rest of the perfusion system was noted in both groups. The mean radial artery pressure did not differ between the groups (74 ± 6 mmHg in Group 1 and 73 ± 6 mmHg in Group 2, p=0.608). Although lower total energy transfer indices were noticed through the low-resistance oxygenator (Group 2), both oxygenators showed a decrease of the generated pump oscillatory energy of approximately 50%. Despite the differences in resistance and compliance of the hollow-fibre oxygenators used, both oxygenators cause a comparable loss of generated oscillatory energy. Exclusion of the oxygenator downstream of the pulsatile pump would improve energy transfer during CPB.

Worldwide, 98% of total electricity is currently produced by thermoelectric power and hydropower. Climate change is expected to directly impact electricity supply, in terms of both water availability for hydropower generation and cooling water usage for thermoelectric power. Improved understanding of how climate change may impact the availability and temperature of water resources is therefore of major importance. Here we use a multi-model ensemble to show the potential impacts of climate change on global hydropower and cooling water discharge potential. For the first time, combined projections of streamflow and water temperature were produced with three global hydrological models (GHMs) to account for uncertainties in the structure and parametrization of these GHMs in both water availability and water temperature. The GHMs were forced with bias-corrected output of five general circulation models (GCMs) for both the lowest and highest representative concentration pathways (RCP2.6 and RCP8.5). The ensemble projections of streamflow and water temperature were then used to quantify impacts on gross hydropower potential and cooling water discharge capacity of rivers worldwide. We show that global gross hydropower potential is expected to increase between +2.4% (GCM-GHM ensemble mean for RCP 2.6) and +6.3% (RCP 8.5) for the 2080s compared to 1971–2000. The strongest increases in hydropower potential are expected for Central Africa, India, central Asia and the northern high-latitudes, with 18–33% of the world population living in these areas by the 2080s. Global mean cooling water discharge capacity is projected to decrease by 4.5-15% (2080s). The largest reductions are found for the United States, Europe, eastern Asia, and southern parts of South America, Africa and Australia, where strong water temperature increases are projected combined with reductions in mean annual streamflow. These regions are expected to affect 11–14% (for RCP2.6 and the shared socio-economic pathway (SSP)1, SSP2, SSP4) and 41–51% (RCP8.5–SSP3, SSP5) of the world population by the 2080s.

Abstract A computational 2-D Eulerian-Eulerian approach was developed to simulate the hydrodynamics and heat transfer of a biomass gasification process in a pilot-scale bubbling fluidized bed reactor. The mathematical model was validated under experimental results collected from fluidization curves gathered at different temperatures in a pilot-scale reactor (75 kWth). Own user defined functions (UDFs) were developed in C programming and included to improve drag and heat transfer phenomena, as well to minimize deviations between experimental and numerical data found in previous works. Mesh selection was achieved by comparing solid fraction and pressure drop contours with grids comprised of different number of cells. A comparative study for particle diameter and inlet gas velocity was conducted for three different biomass feedstocks’ and their impact in the mixing and segregation index was studied. Mixing and segregation index were measured by implementing the standard deviation concept. Results indicated that UDFs significantly improved the mathematical model predictions on the reactor’s fluidization curves. Biomass and sand particles size and density showed direct influence on the solids distribution along the bed height. Smaller biomass particles revealed faster heat conduction and improved mixing properties.

Heating by gas combustion is widespread in residential and industrial environments, through the use of different types of systems and plants. A relevant case is that of gas stoves, where the heat-radiating unit operates autonomously with local gas feeding. A thermoelectric generator (TEG) can be integrated within this type of autonomous gas heater, for local production of electric power, so that devices requiring electric power can be added, where desired, without the need for any connection to the electrical grid. This approach can also lead to easier installation and operation, and eventually increases the overall efficiency. Following the development plan presented in a previous report, a new prototype of an autonomous gas heater for outdoor use has been implemented through the integration of an improved TEG device with a simple and robust design, which can be easily operated by the end-user. A small amount of heat is withdrawn and converted into electricity by the TEG, providing self-sustaining operation and, moreover, powering additional functions such as high-efficiency light-emitting diode lighting.

Maintaining critical data access latency requirements and ensuring efficient energy consumption on the field devices are two important challenges of Industry 4.0. The traditional, centralized industrial networks which offer primitive data distribution functions, might be incapable of meeting such strict requirements. In this paper, in order to overcome this issue, we focus on a network of resource constraint IoT devices, and exploit the presence of a few more capable Edge nodes that act as distributed local data storing proxies for all IoT devices. We show that, given the proxy locations in the network, the initial energy supplies of the nodes, a pattern of data requests from IoT devices, and the maximum access latency that consumer nodes can tolerate, the problem of finding how to distribute the data on the Edge nodes maximizing the network lifetime is computationally hard. We design an offline centralized heuristic algorithm for identifying which paths in the network the data should follow and on which proxies they should be cached, in order to meet the data access latency constraint and to maximize the network lifetime. We implement the method and evaluate its performance using a testbed of IEEE 802.15.4-enabled network nodes. We demonstrate that the proposed heuristic (i) guarantees data access latency below the given threshold, and (ii) performs well in terms of network lifetime with respect to a theoretically optimal solution.