Estimation of the normal boiling point of organic compounds via group contributions

The normal boiling point is a fundamental thermo-physical property, which is important in describing the transition between the vapor and liquid phases. Reliable method which can predict it is of great importance, especially for compounds where there are no experimental data available. In this work, an improved group contribution method, which is second order method, for determination of the normal boiling point of organic compounds based on the Joback functional first order groups with some changes and added some other functional groups was developed by using experimental data for 632 organic components. It could distinguish most of structural isomerism and stereoisomerism, which including the structural, cis- and trans- isomers of organic compounds. First and second order contributions for hydrocarbons and hydrocarbon derivatives containing carbon, hydrogen, oxygen, nitrogen, sulfur, fluorine, chlorine and bromine atoms, are given. The fminsearch mathematical approach from MATLAB software is used in this study to select an optimal collection of functional groups (65 functional groups) and subsequently to develop the model. This is a direct search method that uses the simplex search method of Lagarias et al. The results of the new method are compared to the several currently used methods and are shown to be far more accurate and reliable. The average absolute deviation of normal boiling point predictions for 632 organic compounds is 4.4350 K; and the average absolute relative deviation is 1.1047 %, which is of adequate accuracy for many practical applications.

Reference list of 269 sources used for exploratory search

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

Ecological assessment data of the marine flora and fauna of Point Lookout in 2014 (PLEA)

In 2014, UniDive (The University of Queensland Underwater Club) conducted an ecological assessment of the Point Lookout Dive sites for comparison with similar surveys conducted in 2001. Involvement in the project was voluntary. Members of UniDive who were marine experts conducted training for other club members who had no, or limited, experience in identifying marine organisms and mapping habitats. Since the 2001 detailed baseline study, no similar seasonal survey has been conducted. The 2014 data is particularly important given that numerous changes have taken place in relation to the management of, and potential impacts on, these reef sites. In 2009, Moreton Bay Marine Park was re-zoned, and Flat Rock was converted to a marine national park zone (Green zone) with no fishing or anchoring. In 2012, four permanent moorings were installed at Flat Rock. Additionally, the entire area was exposed to the potential effects of the 2011 and 2013 Queensland floods, including flood plumes which carried large quantities of sediment into Moreton Bay and surrounding waters. The population of South East Queensland has increased from 2.49 million in 2001 to 3.18 million in 2011 (BITRE, 2013). This rapidly expanding coastal population has increased the frequency and intensity of both commercial and recreational activities around Point Lookout dive sites (EPA 2008). Methodology used for the PLEA project was based on the 2001 survey protocols, Reef Check Australia protocols and Coral Watch methods. This hybrid methodology was used to monitor substrate and benthos, invertebrates, fish, and reef health impacts. Additional analyses were conducted with georeferenced photo transects. The PLEA marine surveys were conducted over six weekends in 2014 totaling 535 dives and 376 hours underwater. Two training weekends (February and March) were attended by 44 divers, whilst biological surveys were conducted on seasonal weekends (February, May, July and October). Three reefs were surveyed, with two semi-permanent transects at Flat Rock, two at Shag Rock, and one at Manta Ray Bommie. Each transect was sampled once every survey weekend, with the transect tapes deployed at a depth of 10 m below chart datum. Fish populations were assessed using a visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape), 5 m high and 20 m in length. Fish families and species were chosen that are commonly targeted by recreational or commercial fishers, or targeted by aquarium collectors, and that were easily identified by their body shape. Rare or otherwise unusual species were also recorded. Target invertebrate populations were assessed using visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The diver surveying invertebrates conducted a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Target impacts were assessed using a visual census along the 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The transect was surveyed via a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Substrate surveys were conducted using the point sampling method, enabling percentage cover of substrate types and benthic organisms to be calculated. The substrate or benthos under the transect line was identified at 0.5m intervals, with a 5m gap between each of the three 20m segments. Categories recorded included various growth forms of hard and soft coral, key species/growth forms of algae, other living organisms (i.e. sponges), recently killed coral, and, non-living substrate types (i.e. bare rock, sand, rubble, silt/clay).