Modelling Goal Dependencies and Domain Model Together

AbstractSeveral actors such as human, organization, software applications and hardware units perform our daily activities such as medical care, entertainment and so on. We call each daily activity a socio-technical system (STS), and we also call actors except human and organizations Machines. Human and organizations in an STS become better than ever when new Machines are introduced into the STS and they are beneficial to human and organizations. Although modelling goal dependencies in such a STS contributes to identifying beneficial Machines because such a dependency can represent an actor asks some Machine to achieve his own goal. It is however not easy for modelers to describe a correct dependency. We thus proposed and exemplified an extended modelling notation called Goal Dependency Model with Objects (GDMO) based on strategic dependency (SD) in i*. In GDMO, objects related to a goal in an SD are explicitly specified. Modelers can determine an actor has the right to want the goal to be achieved because relationships between the actor and the objects such as ownership clarify the right. They can also determine another actor has the ability to achieve the goal. In addition, relationships among objects, i.e. a domain model, can suggest missing SDs, and the boundary of an STS can be determined without omission

Detection of Iron (III) Using Agarose Beads Derivatized with Desterrioxamine B

The goal of our work is to provide marine scientists with a detection system which can be mounted on buoys and gliders for measuring the amount of iron (III) in sea water. Iron is the limiting nutrient for phytoplankton growth. Since phytoplankton play a key role in global carbon cycles and global warming, the iron in seawater does as well. This thesis is meant to be a proof-of-concept for a new approach to measuring iron in seawater. The essential elements of the research discussed herein include identifying particulate beads which are optically transparent in the visible region, modifying the surfaces of these beads to reversibly bind iron (III) from water, and measuring the detection limit directly on the transparent beads using UV-Visible (UV-Vis) spectroscopy. Agarose beads were selected and shown to be semi-transparent in water. These beads were treated with polystyrene50-b-poly(acrylic acid)180; the acrylic acid portion of the polymer was then reacted with iron (III) chelator desferrioxamine B using EDC as a catalyst. Infrared spectroscopy was used to show that the block copolymer and DFB had attached to the beads. UV-Vis spectroscopy was used to study the iron uptake by these beads, using the red color produced when DFB chelates iron (III). The amount of time needed for the beads to take up iron (III) from a solution and the saturation point of the beads was also determined. It was shown that detection of low parts per trillion is possible and that at pH 7.5, the presence of oxalate would not affect iron (III) uptake

Detection of Iron (III) Using Magnetic Nanoparticles

The goal of this work was to develop an iron (III) sensor which can be mounted on buoys and gliders and automatically measure iron (III) in the ocean. The method investigated in this thesis was based on anchoring the iron (III) chelator desferrioxamine B (DFB) to magnetic particles. DFB selectively binds iron (III), resulting in a complex which produces a broad absorbance band at 430 nm. This band can be measured via UV-Vis spectroscopy (UV-Vis). However, measuring oceanic iron (III) directly in solution is difficult because the concentrations can be very low, sometimes less than 1 nM. This problem could be solved if one were able to concentrate the iron (III) from a sample and magnetic particles (which can be removed from a solution by introducing a magnet to the side of the container) were used to accomplish this task. In Chapter 2, it was found that the untreated carbon-coated cobalt magnetic (Co‑C) particles were able to capture iron (III) from a solution. This offered the opportunity to use untreated Co‑C particles to capture iron (III) and then in a second step, remove the iron (III) from the particles for analysis in solution. Most of the studies in Chapter 3 were directed at developing a protocol for extracting iron (III) from the untreated Co‑C particles. In one case, 100% of the iron (III) was removed from the particles with DFB. However, this result was not repeatable – in all other cases, 72% or less of the iron (III) captured from solution was recovered by the DFB. It was determined that this was because the Co‑C particles were exposed to the air between removing the remaining iron solution and adding the DFB. This allowed the iron (III) on the surface to react and form iron oxyhydroxides, meaning that the DFB was not able to capture it all. In Chapter 4, a variety of magnetic nanoparticles were derivatized with DFB on the surface: Co‑C particles, TurboBeads Silica™ (Co‑C particles purchased with a silica coating), and silica-coated nickel nanoparticles (Ni‑SiO2). It was found that TurboBeads Silica™ treated with DFB or DFB‑Fe (DFB already bound to iron), as well as Ni‑SiO2 treated with DFB-Fe could capture iron (III) from solution. Some of the iron (III) could be removed from the particles by adding oxalate adjusted to a pH of 1.5. However, no more than 70% (usually less) of the iron (III) captured by these particles was ultimately recovered by the oxalate. The DFB on the particles’ surface should have captured the iron (III), preventing it from forming oxyhydroxides. However, to measure iron (III) in the oxalate required adding DFB and adjusting the pH to form the DFB-Fe complex in solution. It was discovered that in a solution of oxalate, DFB-Fe can be reduced to iron (II) when exposed to light. Because measurements were not performed in a dark room, some of the iron (III) was rendered undetectabl

David

A photographic study that encapsulates my friend David