Preliminary study of the effects of carbon nanotubes on Cpu cooling

Managing heat produced by computer processors is an important issue today, especially when the size of processors is decreasing rapidly while the number of transistors in the processor is increasing rapidly. This poster describes a preliminary study of the process of adding carbon nanotubes (CNTs) to a standard silicon paste covering a CPU. Measurements were made in two rounds of tests to compare the rate of cool-down with and without CNTs present. The silicon paste acts as an interface between the CPU and the heat sink, increasing the heat transfer rate away from the CPU. To the silicon paste was added 0.05% by weight of CNTs. These were not aligned. A series of K-type thermocouples was used to measure the temperature as a function of time in the vicinity of the CPU, following its shut-off. An Omega data acquisition system was attached to the thermocouples. The CPU temperature was not measured directly because attachment of a thermocouple would have prevented its automatic shut-off A thermocouple in the paste containing the CNTs actually reached a higher temperature than the standard paste, an effect easily explained. But the rate of cooling with the CNTs was about 4.55% better.

CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change

Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25ha), all stems 1cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25 degrees S-61 degrees N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 degrees C), changes in precipitation (up to +/- 30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8g Nm(-2)yr(-1) and 3.1g Sm(-2)yr(-1)), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS-ForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.