Computer vision and color measurement techniques for inline monitoring of cheese curd syneresis

Optical characteristics of stirred curd were simultaneously monitored during syneresis in a 10-L cheese vat using computer vision and colorimetric measurements. Curd syneresis kinetic conditions were varied using 2 levels of milk pH (6.0 and 6.5) and 2 agitation speeds (12.1 and 27.2 rpm). Measured optical parameters were compared with gravimetric measurements of syneresis, taken simultaneously. The results showed that computer vision and colorimeter measurements have potential for monitoring syneresis. The 2 different phases, curd and whey, were distinguished by means of color differences. As syneresis progressed, the backscattered light became increasingly yellow in hue for circa 20 min for the higher stirring speed and circa 30 min for the lower stirring speed. Syneresis-related gravimetric measurements of importance to cheese making (e.g., curd moisture content, total solids in whey, and yield of whey) correlated significantly with computer vision and colorimetric measurements..

Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research

The effectiveness of development assistance has come under renewed scrutiny in recent years. In an era of growing economic liberalisation, research organisations are increasingly being asked to account for the use of public funds by demonstrating achievements. However, in the natural resources (NR) research field, conventional economic assessment techniques have focused on quantifying the impact achieved rather understanding the process that delivered it. As a result, they provide limited guidance for planners and researchers charged with selecting and implementing future research. In response, “pathways” or logic models have attracted increased interest in recent years as a remedy to this shortcoming. However, as commonly applied these suffer from two key limitations in their ability to incorporate risk and assess variance from plan. The paper reports the results of a case study that used a Bayesian belief network approach to address these limitations and outlines its potential value as a tool to assist the planning, monitoring and evaluation of development-orientated research.

The use of bioluminescence for monitoring in planta growth dynamics of a Pseudomonas syringae plant pathogen

The use of bioluminescence was evaluated as a tool to study Pseudomonas syringae population dynamics in susceptible and resistant plant environments. Plasmid pGLITE, containing the luxCDABE genes from Photorhabdus luminescens, was introduced into Pseudomonas syringae pv. phaseolicola race 7 strain 1449B, a Gram-negative pathogen of bean (Phaseolus vulgaris). Bacteria recovered from plant tissue over a five-day period were enumerated by counting numbers of colony forming units and by measurement of bioluminescence. Direct measurement of bioluminescence from leaf disc homogenates consistently reflected bacterial growth as determined by viable counting, but also detected subtle effects of the plant resistance response on bacterial viability. This bioluminescence procedure enables real time measurement of bacterial metabolism and population dynamics in planta, obviates the need to carry out labour intensive and time consuming traditional enumeration techniques and provides a sensitive assay for studying plant effects on bacterial cells.

Monitoring growth of hard corals as performance indicators for coral reefs

Digital videophotography, computer image analysis and physical measurements have been used to monitor sedimentation rates, coral cover, genera richness, rugosity and estimated recruitment dates of massive corals at three different sites in the Wakatobi Marine National Park, Indonesia, and on the reefs around Discovery Bay, Jamaica. Semi-structured interviews with key stakeholders in the Wakatobi Marine National Park indicated that coral mining was extensively practised, and is responsible for the absence of large non-branching corals on the Sampela reef Blast fishing is also practised in the Wakatobi Marine Park, and the authors, together with students, showed that blast fishing resulted in coral bleaching and not mortality of two Porites lutea colonies. In addition, we showed that monitoring of bleaching in Porites colonies induced by blast fishing could be a useful way of monitoring blast fishing practices in susceptible areas in the Indo-Pacific. The techniques used in this study are appropriate for use by volunteers with sufficient training, and provide excellent projects for dissertation students reading undergraduate degrees.

Classification and fault detection methods for fuel cell monitoring and quality control

In this paper, various types of fault detection methods for fuel cells are compared. For example, those that use a model based approach or a data driven approach or a combination of the two. The potential advantages and drawbacks of each method are discussed and comparisons between methods are made. In particular, classification algorithms are investigated, which separate a data set into classes or clusters based on some prior knowledge or measure of similarity. In particular, the application of classification methods to vectors of reconstructed currents by magnetic tomography or to vectors of magnetic field measurements directly is explored. Bases are simulated using the finite integration technique (FIT) and regularization techniques are employed to overcome ill-posedness. Fisher's linear discriminant is used to illustrate these concepts. Numerical experiments show that the ill-posedness of the magnetic tomography problem is a part of the classification problem on magnetic field measurements as well. This is independent of the particular working mode of the cell but influenced by the type of faulty behavior that is studied. The numerical results demonstrate the ill-posedness by the exponential decay behavior of the singular values for three examples of fault classes.

Rule Type Identification using TRCM for trend analysis in Twitter

This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.