Microsensors technology used to measure productivity and other biochemical processes in the upper regions of aquatic sediments

Carbonate sediments are dynamic three-dimensional environments where the surface layers are constantly moving and mixing due to the energy of the water column. It is also an environment of dynamic biological, chemical and physical interaction and modification. The biological community can actively influence changes to sediment characteristics and associated biochemistry. Bioturbation resulting from macrofaunal activity disrupts sediment structure and biochemical arrangements and reduces the critical shear forces required to move sediment particles, adding to the dynamic and complex physical and biogeochemical nature of the sediment. Laboratory studies using both planner optodes and glass needle microsensors were used to measure abiotic sediment characteristics such as the depth distribution and concentrations of PAR. The biochemical nature of coral reef sediment were also investigated, specifically the quantification and the distribution of dissolved oxygen within coarse and fine-grained sediments under regimes of light and darkness. Results highlighted the significant contribution microalgal productivity and bioturbation has on distribution of dissolved oxygen in the upper sediment layers. On the reef flat a shallow water lander system was employed to measure concentrations of O2, pH, S, Ca and temperature over periods of 24 to 48 hours in coarse and fine-grained sediments. Similarities between laboratory and in situ results where evident, however the in situ environment was more dynamic and the distribution and concentrations of dissolved oxygen were more complex and correlated to irradiance, temperature and biological activity. Microsensor technology provides us with the opportunity to study, at very high resolutions, the upper irradiated; photosynthetically active regions of aquatic sediments along with anoxic processes deeper in sub-euphotic regions of the sediments.

Minimum description length, regularisation and multi-modal data

Relationships between clustering, description length, and regularisation are pointed out, motivating the introduction of a cost function with a description length interpretation and the unusual and useful property of having its minimum approximated by the densest mode of a distribution. A simple inverse kinematics example is used to demonstrate that this property can be used to select and learn one branch of a multi-valued mapping. This property is also used to develop a method for setting regularisation parameters according to the scale on which structure is exhibited in the training data. The regularisation technique is demonstrated on two real data sets, a classification problem and a regression problem.

Channel incentives as unilateral and bilateral governance processes

The authors investigate channel incentives as extra-contractual governance processes that maintain and extend marketing channel relationships. More specifically, instrumental incentives are monetary-based payments made by a manufacturer in a unilateral channel arrangement to motivate distributor compliance, while equity incentives are bilateral expectations of fair treatment that motivate both parties to continue to cooperate with one another. A model of the antecedents and performance consequences of channel incentives is conceptualized and tested on 314 marketing channel relationships using a structural equation modeling methodology. The findings support the conceptual model and suggest that unique facets of the channel relationship explain the type of incentive mechanism in use.

Fiber Bragg grating structures inscribed using 800nm femtosecond laser in single- and multi-core mid-IR glass fibers with their spectral, thermal and strain properties

Single- and multi-core passive and active germanate and tellurite glass fibers represent a new class of fiber host for in-fiber photonics devices and applications in mid-IR wavelength range, which are in increasing demand. Fiber Bragg grating (FBG) structures have been proven as one of the most functional in-fiber devices and have been mass-produced in silicate fibers by UV-inscription for almost countless laser and sensor applications. However, because of the strong UV absorption in germanate and tellurite fibers, FBG structures cannot be produced by UVinscription. In recent years femtosecond (fs) lasers have been developed for laser machining and microstructuring in a variety of glass fibers and planar substrates. A number of papers have been reported on fabrication of FBGs and long-period gratings in optical fibers and also on the photosensitivity mechanism using 800nm fs lasers. In this paper, we demonstrate for the first time the fabrication of FBG structures created in passive and active single- and three-core germanate and tellurite glass fibers by using 800nm fs-inscription and phase mask technique. With a fs peak power intensity in the order of 1011W/cm2, the FBG spectra with 2nd and 3rd order resonances at 1540nm and 1033nm in a single-core germanate glass fiber and 2nd order resonances between ~1694nm and ~1677nm with strengths up to 14dB in all three cores of three-core passive and active tellurite fibers were observed. Thermal and strain properties of the FBGs made in these mid-IR glass fibers were characterized, showing an average temperature responsivity of ~20pm/°C and a strain sensitivity of 1.219±0.003pm/µe.

Modelling and benchmarking business processes: The supply-line example

Benchmarking techniques have evolved over the years since Xerox’s pioneering visits to Japan in the late 1970s. The focus of benchmarking has also shifted during this period. By tracing in detail the evolution of benchmarking in one specific area of business activity, supply and distribution management, as seen by the participants in that evolution, creates a picture of a movement from single function, cost-focused, competitive benchmarking, through cross-functional, cross-sectoral, value-oriented benchmarking to process benchmarking. As process efficiency and effectiveness become the primary foci of benchmarking activities, the measurement parameters used to benchmark performance converge with the factors used in business process modelling. The possibility is therefore emerging of modelling business processes and then feeding the models with actual data from benchmarking exercises. This would overcome the most common criticism of benchmarking, namely that it intrinsically lacks the ability to move beyond current best practice. In fact the combined power of modelling and benchmarking may prove to be the basic building block of informed business process re-engineering.

Minimum description length, regularisation and multi-modal data

Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.

Response normalization and blur adaptation:data and multi-scale model

Adapting to blurred or sharpened images alters perceived blur of a focused image (M. A. Webster, M. A. Georgeson, & S. M. Webster, 2002). We asked whether blur adaptation results in (a) renormalization of perceived focus or (b) a repulsion aftereffect. Images were checkerboards or 2-D Gaussian noise, whose amplitude spectra had (log-log) slopes from -2 (strongly blurred) to 0 (strongly sharpened). Observers adjusted the spectral slope of a comparison image to match different test slopes after adaptation to blurred or sharpened images. Results did not show repulsion effects but were consistent with some renormalization. Test blur levels at and near a blurred or sharpened adaptation level were matched by more focused slopes (closer to 1/f) but with little or no change in appearance after adaptation to focused (1/f) images. A model of contrast adaptation and blur coding by multiple-scale spatial filters predicts these blur aftereffects and those of Webster et al. (2002). A key proposal is that observers are pre-adapted to natural spectra, and blurred or sharpened spectra induce changes in the state of adaptation. The model illustrates how norms might be encoded and recalibrated in the visual system even when they are represented only implicitly by the distribution of responses across multiple channels.