Michigan Tech Lode Pictorial

Vol 2 No 3; 8 February 1964. Land of the Sandman.

RESPONSE OF ECTOMYCORRHIZAL FUNGI TO INORGANIC AND ORGANIC FORMS OF NITROGEN AND PHOSPHORUS

The nutrient uptake response of ectomycorrhizal fungi (ECM) to different nutrient substrates is a driving force in ecosystem nutrient cycling. We hypothesized that taxa from low nitrogen (N) soils would be more likely to use organic N compared to taxa from high N soils, and that taxa from high N would be more likely to use organic phosphorus (P) sources when compared to the ECM dominant in low N soils. This study focuses on the growth response of ECM species collected over a N gradient to different forms of N and P nutrient substrates and whether ECM growth in a particular nutrient source can be related to how the ECM fungi have responded to elevated N in the field. This study found a mixed ECM response to organic and inorganic N and P treatments. High affinity N taxa expected to respond positively to inorganic N produced the phosphatase enzyme to take up organic phosphorus, but not all low affinity N taxa expected to negatively respond to organic P produced the protease enzyme to take up organic N. Interspecific variability was displayed by some high and low affinity N taxa responded and ECM intraspecific variability in response to N and P treatments was also noted. Future analysis of may show more evident ECM response patterns to inorganic and organic forms of N and P.

EXTRACTION OF PICTORIAL ENERGY INFORMATION FROM CAMPUS UNMETERED BUILDINGS USING IMAGE PROCESSING TECHNIQUES

In recent years, advanced metering infrastructure (AMI) has been the main research focus due to the traditional power grid has been restricted to meet development requirements. There has been an ongoing effort to increase the number of AMI devices that provide real-time data readings to improve system observability. Deployed AMI across distribution secondary networks provides load and consumption information for individual households which can improve grid management. Significant upgrade costs associated with retrofitting existing meters with network-capable sensing can be made more economical by using image processing methods to extract usage information from images of the existing meters. This thesis presents a new solution that uses online data exchange of power consumption information to a cloud server without modifying the existing electromechanical analog meters. In this framework, application of a systematic approach to extract energy data from images replaces the manual reading process. One case study illustrates the digital imaging approach is compared to the averages determined by visual readings over a one-month period.