Protection of corneal endothelium from irrigation damage:A comparison of sodium hyaluronate and hydroxypropylmethyl-cellulose

Purpose: To compare the endothelial protection of sodium hyaluronate and hydroxypropylmethylcellulose against endothelial damage induced by irrigation. Methods: An in vitro assay with freshly excised porcine eyes was developed using the Janus green photometry technique. Irrigation and aspiration technique was standardised. Forty pairs of porcine eyes were used. One randomly chosen eye was filled with sodium hyaluronate (SH) and the other with hydroxypropylmethylcellulose (HPMC). Irrigation and aspiration was carried out with balanced salt solution for 5 min. Twenty additional pairs of porcine eyes served as controls. Student's t-test was used for statistical analysis. Results: Both viscoelastic agents protected the endothelium as compared with controls. The endothelial protection, determined with the Janus green photometric technique, was significantly greater with HPMC than with SH. Conclusions: Viscoelastic agents are effective in protecting the endothelium from irrigation damage in porcine eyes in vitro. HPMC provided greater protection than SH in this particular model.

Alternate wetting and drying irrigation for rice in Bangladesh: Is it sustainable and has plant breeding something to offer?

The crop management practice of alternate wetting and drying (AWD) is being promoted by IRRI and the national research and extension program in Bangladesh and other parts of the world as a water-saving irrigation practice that reduces the environmental impact of dry season rice production through decreased water usage, and potentially increases yield. Evidence is growing that AWD will dramatically reduce the concentration of arsenic in harvested rice grains conferring a third major advantage over permanently flooded dry season rice production. AWD may also increase the concentration of essential dietary micronutrients in the grain. However, three crucial aspects of AWD irrigation require further investigation. First, why is yield generally altered in AWD? Second, is AWD sustainable economically (viability of farmers' livelihoods) and environmentally (aquifer water table heights) over long-term use? Third, are current cultivars optimized for this irrigation system? This paper describes a multidisciplinary research project that could be conceived which would answer these questions by combining advanced soil biogeochemistry with crop physiology, genomics, and systems biology. The description attempts to show how the breakthroughs in next generation sequencing could be exploited to better utilize local collections of germplasm and identify the molecular mechanisms underlying biological adaptation to the environment within the context of soil chemistry and plant physiology.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.