Towards novel compact laser sources for non-invasive diagnostics and treatment

An important field of application of lasers is biomedical optics. Here, they offer great utility for diagnosis, therapy and surgery. For the development of novel methods of laser-based biomedical diagnostics careful study of light propagation in biological tissues is necessary to enhance our understanding of the optical measurements undertaken, increase research and development capacity and the diagnostic reliability of optical technologies. Ultimately, fulfilling these requirements will increase uptake in clinical applications of laser based diagnostics and therapeutics. To address these challenges informative biomarkers relevant to the biological and physiological function or disease state of the organism must be selected. These indicators are the results of the analysis of tissues and cells, such as blood. For non-invasive diagnostics peripheral blood, cells and tissue can potentially provide comprehensive information on the condition of the human organism. A detailed study of the light scattering and absorption characteristics can quickly detect physiological and morphological changes in the cells due to thermal, chemical, antibiotic treatments, etc [1-5]. The selection of a laser source to study the structure of biological particles also benefits from the fact that gross pathological changes are not induced and diagnostics make effective use of the monochromatic directional coherence properties of laser radiation.

A hybrid measurement systems analysis and uncertainty of measurement approach for industrial measurement in the light controlled factory

The uncertainty of measurements must be quantified and considered in order to prove conformance with specifications and make other meaningful comparisons based on measurements. While there is a consistent methodology for the evaluation and expression of uncertainty within the metrology community industry frequently uses the alternative Measurement Systems Analysis methodology. This paper sets out to clarify the differences between uncertainty evaluation and MSA and presents a novel hybrid methodology for industrial measurement which enables a correct evaluation of measurement uncertainty while utilising the practical tools of MSA. In particular the use of Gage R&R ANOVA and Attribute Gage studies within a wider uncertainty evaluation framework is described. This enables in-line measurement data to be used to establish repeatability and reproducibility, without time consuming repeatability studies being carried out, while maintaining a complete consideration of all sources of uncertainty and therefore enabling conformance to be proven with a stated level of confidence. Such a rigorous approach to product verification will become increasingly important in the era of the Light Controlled Factory with metrology acting as the driving force to achieve the right first time and highly automated manufacture of high value large scale products such as aircraft, spacecraft and renewable power generation structures.

Sources of metals and sulfur in the mineral deposits of central Idaho, USA

A variety of mineral deposits occur in the Paleozoic sedimentary rocks and Late Cretaceous granitic rocks of central Idaho. The main objective of this project is to identify the sources of metals and sulfur in central Idaho ores. Lead isotope compositions of various crustal rocks were determined and compared with the ore lead composition in order to trace sources of lead, and by inference other metals. Sulfur isotope compositions of various sulfide minerals were also determined to trace the sources of sulfur and to explore the coupling or decoupling of metal and sulfur sources. ^ On the basis of lead and sulfur isotope compositions, two groups of ores are recognized: a sedimentary group and an igneous group. The sedimentary group ores are characterized by radiogenic lead and heavy sulfur typical of upper crustal rocks. The sedimentary group ores were formed by meteoric water-dominated hydrothermal systems that leached metals and sulfur from host Paleozoic sedimentary rocks and the underlying Precambrian crystalline basement rocks. The igneous group ores can be divided into two types, the Carrietown-type, and the non Carrietown-type. The Carrietown-type ores are isotopically different from their host granites and are characterized by low uranogenic lead isotope ratios (206Pb/204Pb and 207Pb/ 204Pb) and variable thorogenic lead isotope ratios (208Pb/ 204Pb) typical of lower crustal rocks. The non Carrietown-type ores are similar to host granites and are more radiogenic in their uranogenic lead isotope ratios when compared to the Carrietown-type ores. The differences in the lead isotope compositions of the igneous group ores are attributed to two different phases of magmatic activity. The magmatic phase exposed on the surface involved melting of shallow crustal Precambrian crystalline rocks as well as mid/lower crustal rocks while the underlying phase was derived by melting of mid/lower crustal rocks only. Igneous group ores have both light and heavy sulfur associated with them and it is a function of interaction of hydrothermal fluids with Paleozoic sedimentary rocks. ^ Paleozoic sedimentary rocks and Precambrian basement rocks are the sources of radiogenic lead, and the granites are the sources of light sulfur. Heavy sulfur comes almost entirely from Paleozoic sedimentary rocks. ^

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

The impact of eliminating extraneous sound and light on students' achievement: An empirical study

The impact of eliminating extraneous sound and light on students’ achievement was investigated under four conditions: Light and Sound controlled, Sound Only controlled, Light Only controlled and neither Light nor Sound controlled. Group, age and gender were the control variables. Four randomly selected groups of high school freshmen students with different backgrounds were the participants in this study. Academic achievement was the dependent variable measured on a pretest, a posttest and a post-posttest, each separated by an interval of 15 days. ANOVA was used to test the various hypotheses related to the impact of eliminating sound and light on student learning. Independent sample T tests on the effect of gender indicated a significant effect while age was non- significant. Follow up analysis indicated that sound and light are not potential sources of extraneous load when tested individually. However, the combined effect of sound and light seems to be a potential source of extrinsic load. The findings revealed that the performance of the Sound and Light controlled group was greater during the posttest and post-posttest. The overall performance of boys was greater than that of girls. Results indicated a significant interaction effect between group and gender on treatment subjects. However gender alone was non-significant. Performance of group by age had no significant interaction and age alone was non-significant in the posttest and post-posttest. Based on the results obtained sound and light combined seemed to be the potential sources of extraneous load in this type of learning environment. This finding supports previous research on the effect of sound and light on learning. The findings of this study show that extraneous sound and light have an impact on learning. These findings can be used to design better learning environments. Such environments can be achieved with different electric lighting and sound systems that provide optimal color rendering, low glare, low flicker, low noise and reverberation. These environments will help people avoid unwanted distraction, drowsiness, and photosensitive behavior.

Mosaic image of the artificial light produced by Berlin on the night of September 11, 2010 observed from an altitude of 10,000 ft

Aerial observations of light pollution can fill an important gap between ground based surveys and nighttime satellite data. Terrestrially bound surveys are labor intensive and are generally limited to a small spatial extent, and while existing satellite data cover the whole world, they are limited to coarse resolution. This paper describes the production of a high resolution (1 m) mosaic image of the city of Berlin, Germany at night. The dataset is spatially analyzed to identify themajor sources of light pollution in the city based on urban land use data. An area-independent 'brightness factor' is introduced that allows direct comparison of the light emission from differently sized land use classes, and the percentage area with values above average brightness is calculated for each class. Using this methodology, lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%), although other land use classes have much higher average brightness. These results are compared with other urban light pollution quantification studies. The minimum resolution required for an analysis of this type is found to be near 10 m. Future applications of high resolution datasets such as this one could include: studies of the efficacy of light pollution mitigation measures, improved light pollution simulations, economic and energy use, the relationship between artificial light and ecological parameters (e.g. circadian rhythm, fitness, mate selection, species distributions, migration barriers and seasonal behavior), or the management of nightscapes. To encourage further scientific inquiry, the mosaic data is freely available at Pangaea.

Seawater carbonate chemistry and effects of CO2 and their modulation by light in the life-cycle stages of the coccolithophore Emiliania huxleyi strains RCC 1216 and 1217 during experiments, 2012

The effects of ocean acidification on the life-cycle stages of the coccolithophore Emiliania huxleyi and their by light were examined. Calcifying diploid and noncalcifying haploid cells (Roscoff culture collection 1216 and 1217) were acclimated to present-day and elevated CO2 partial pressures (PCO2; 38.5 vs. 101.3 Pa, ., 380 vs. 1000 matm) under low and high light (50 vs. 300 mmol photons m-2 s-1). Growth rates as well as quotas and production rates of C and N were measured. Sources of inorganic C for biomass buildup were using a 14C disequilibrium assay. Photosynthetic O2 evolution was measured as a function of dissolved inorganic C and light by means of membrane-inlet mass spectrometry. The diploid stage responded to elevated PCO2 by shunting resources from the production of particulate inorganic C toward organic C yet keeping the production of total particulate C constant. As the effect of ocean acidification was stronger under low light, the diploid stage might be less affected by increased acidity when energy availability is high. The haploid stage maintained elemental composition and production rates under elevated PCO2. Although both life-cycle stages involve different ways of dealing with elevated PCO2, the responses were generally modulated by energy availability, being typically most pronounced under low light. Additionally, PCO2 responses resembled those induced by high irradiances, indicating that ocean acidification affects the interplay between energy-generating processes (photosynthetic light reactions) and processes competing for energy (biomass buildup and calcification). A conceptual model is put forward explaining why the magnitude of single responses is determined by energy availability.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.