Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

The analysis of the resort timesharing industry in order to determine the industry's familiarity with timesharing and the industry's conception of the present and future effects of timesharing

The purpose of the study is to examine the impactof the timesharing concept on the resort industry in order to determine the industry's familiarity with timesharing and the industry's conception of the present and future effects of timesharing. The study utilizes two methods of research, primarydata and secondary data, to examine the concept of timesharing. This section includes information on the different forms of timesharing, the legal aspects, the marketing, management, finance and future of timesharing in order to educate the public about the concept. The primary data takes the form of a survey thatquestions hotel/motel operators in the Fort Lauderdale Beach area to determine their attitudes towards the impact of timesharing on the resort industy.

The potential of solid phase microextraction (spme) for the recovery of explosives followed by gas and liquid chromatographic analysis

The potential of solid phase microextraction (SPME) in the analysis of explosives is demonstrated. A sensitive, rapid, solventless and inexpensive method for the analysis of explosives and explosive odors from solid and liquid samples has been optimized using SPME followed by HPLC and GC/ECD. SPME involves the extraction of the organic components in debris samples into sorbent-coated silica fibers, which can be transferred directly to the injector of a gas chromatograph. SPME/HPLC requires a special desorption apparatus to elute the extracted analyte onto the column at high pressure. Re suits for use of GC[ECD is presented and compared to the results gathered by using HPLC analysis. The relative effects of controllable variables including fiber chemistry, adsorption and desorption temperature, extraction time, and desorption time have been optimized for various high explosives.

Utilization of needs assessments by decision makers : a comparative analysis of assessment methods

The major purpose of this study was to ascertain how needs assessment findings and methodologies are accepted by public decision makers in the U. S. Virgin Islands. To accomplish this, the following five different needs assessments were executed: (1) population survey; (2) key informants survey; (3) community forum; (4) rates-under-treatment (RUT); and (5) social indicators analysis. The assessments measured unmet needs of older persons regarding transportation, in-home care, and sociorecreation services, and determined which of the five methodologies is most costly, time consuming, and valid. The results of a five-way comparative analysis was presented to public sector decision makers who were surveyed to determine whether they are influenced more by needs assessment findings, or by the methodology used, and to ascertain the factors that lead to their acceptance of needs assessment findings and methodologies. The survey results revealed that acceptance of findings and methodology is influenced by the congruency of the findings with decision makers' goals and objectives, feasibility of the findings, and credibility of the researcher. The study also found that decision makers are influenced equally by needs assessment findings and methodology; that they prefer population surveys, although they are the most expensive and time consuming of the methodologies; that different types of needs assessments produce different results; and, that needs assessment is an essential program planning tool. Executive decision makers are found to be influenced more by management factors than by legal and political factors, while legislative decision makers are influenced more by legal factors. Decision makers overwhelmingly view their leadership style as democratic. A typology of the five needs assessments, highlighting their strengths and weaknesses is offered as a planning guide for public decision makers.

EPOCA/EUR-OCEANS data compilation on the effects of ocean acidification, 2011

The uptake of anthropogenic CO2 by the oceans has led to a rise in the oceanic partial pressure of CO2, and to a decrease in pH and carbonate ion concentration. This modification of the marine carbonate system is referred to as ocean acidification. Numerous papers report the effects of ocean acidification on marine organisms and communities but few have provided details concerning full carbonate chemistry and complementary observations. Additionally, carbonate system variables are often reported in different units, calculated using different sets of dissociation constants and on different pH scales. Hence the direct comparison of experimental results has been problematic and often misleading. The need was identified to (1) gather data on carbonate chemistry, biological and biogeochemical properties, and other ancillary data from published experimental data, (2) transform the information into common framework, and (3) make data freely available. The present paper is the outcome of an effort to integrate ocean carbonate chemistry data from the literature which has been supported by the European Network of Excellence for Ocean Ecosystems Analysis (EUR-OCEANS) and the European Project on Ocean Acidification (EPOCA). A total of 185 papers were identified, 100 contained enough information to readily compute carbonate chemistry variables, and 81 data sets were archived at PANGAEA - The Publishing Network for Geoscientific & Environmental Data. This data compilation is regularly updated as an ongoing mission of EPOCA.

Multi-year weather data in the remote semi-arid to arid mountain region of the Saharan flank of the Atlas Mountains

In 2001, a weather and climate monitoring network was established along the temperature and aridity gradient between the sub-humid Moroccan High Atlas Mountains and the former end lake of the Middle Drâa in a pre-Saharan environment. The highest Automated Weather Stations (AWS) was installed just below the M'Goun summit at 3850 m, the lowest station Lac Iriki was at 450 m. This network of 13 AWS stations was funded and maintained by the German IMPETUS (BMBF Grant 01LW06001A, North Rhine-Westphalia Grant 313-21200200) project and since 2011 five stations were further maintained by the GERMAN DFG Fennec project (FI 786/3-1), this way some stations of the AWS network provided data for almost 12 years from 2001-2012. Standard meteorological variables such as temperature, humidity, and wind were measured at an altitude of 2 m above ground. Other meteorological variables comprise precipitation, station pressure, solar irradiance, soil temperature at different depths and for high mountain station snow water equivalent. The stations produced data summaries for 5-minute-precipitation-data, 10- or 15-minute-data and a daily summary of all other variables. This network is a unique resource of multi-year weather data in the remote semi-arid to arid mountain region of the Saharan flank of the Atlas Mountains. The network is described in Schulz et al. (2010) and its further continuation until 2012 is briefly discussed in Redl et al. (2015, doi:10.1175/MWR-D-15-0223.1) and Redl et al. (2016, doi:10.1002/2015JD024443).

Data on the biogeochemical cycle of methane in the ocean

Geological, mineralogical and microbiological aspects of the methane cycle in water and sediments of different areas in the oceans are under consideration in the monograph. Original and published estimations of formation- and oxidation rates of methane with use of radioisotope and isotopic methods are given. The role of aerobic and anaerobic microbial oxidation of methane in production of organic matter and in formation of authigenic carbonates is considered. Particular attention is paid to processes of methane transformation in areas of its intensive input to the water column from deep-sea hydrothermal sources, mud volcanoes, and cold methane seeps.

Age of bottom sediments from the South Atlantic Rise as indicated by 14C data for stations of R/V Professor Shtokman

Sedimentation of pelagic biogenic coccolithic-foraminiferal sediments predominates in the section of the South Atlantic ridge between 20° and 30°S. Sedimentation rate and thickness of Late Quaternary sediments differ in the rift valley, the crestal section of the ridge, its flanks and transform faults. Holocene and layers representing the most recent and pen¬ultimate continental glaciations and the last interglacial are distinguishable in the late Quaternary profile. During their development, changes in the mean annual sea surface temperature in the tropical zone of the South Atlantic were minimal, i.e. 1-2°C.

Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall-runoff model

Acknowledgements The authors would like to thank Jonathan Dick, Josie Geris, Jason Lessels, and Claire Tunaley for data collection and Audrey Innes for lab sample preparation. We also thank Christian Birkel for discussions about the model structure and comments on an earlier draft of the paper. Climatic data were provided by Iain Malcolm and Marine Scotland Fisheries at the Freshwater Lab, Pitlochry. Additional precipitation data were provided by the UK Meteorological Office and the British Atmospheric Data Centre (BADC).We thank the European Research Council ERC (project GA 335910 VEWA) for funding the VeWa project.

Using population-based critical care data to evaluate trauma outcomes

Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved. Acknowledgements We would like to thank the Scottish Intensive Care Society Audit Group (SICSAG) for providing the data for this study. Mr Jan Jansen is in receipt of an NHS Research Scotland fellowship which includes salary funding.

Using population-based critical care data to evaluate trauma outcomes

Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved. Acknowledgements We would like to thank the Scottish Intensive Care Society Audit Group (SICSAG) for providing the data for this study. Mr Jan Jansen is in receipt of an NHS Research Scotland fellowship which includes salary funding.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

A phenomenological study to explore individuals’ experience of depression

Numerous epidemiological findings suggest that we live in an era that can only be described as the “age of melancholy” in that more and more individuals are diagnosed with depression every year. The aim of this study was to gain a phenomenological understanding of how individuals who experienced depression understood and made sense of their experience of depression through a methodology of interpretative phenomenological analysis. In-depth semi-structured interviews explored the lived experience of depression for eight individuals and identified how social discourses contributed to their understanding. Following rigorous analysis of twelve interview transcripts, data was broken down into four recurrent superordinate themes which related directly to how individuals made sense of their experience of depression; The Descent; The Worlds Conversations and Me - Engagement with Social Discourses; Broken Self - Transforming the Self; Embracing myself and my Mind - Transformation of the Self. Further interrogative analysis identified how some social discourses communicated by healthcare professionals, the media and academia, contributed to individuals experiencing an additional layer of distress, namely meta-distress which in essence is distress about distress.