Tracking and identifying wastewater toxicants and assessing their biodegradation products

Screening of wastewater effluents from municipal and industrial wastewater treatment plants with biotests showed that the treated wastewater effluents possess only minor acute toxic properties towards whole organisms (e.g. bacteria, algae, daphnia), if any. In vitro tests (sub-mitochondrial membranes and fish hepatocytes) were generally more susceptible to the effluents. Most of the effluents indicated the presence of hormonally active compounds, as the production of vitellogenin, an egg yolk precursor protein, was induced in fish hepatocytes exposed to wastewater. In addition, indications of slight genotoxic potential was found in one effluent concentrate with a recombinant bacteria test. Reverse electron transport (RET) of mitochondrial membranes was used as a model test to conduct effluent assessment followed by toxicant characterisations and identifications. Using a modified U.S. EPA Toxicity Identification Evaluation Phase I scheme and additional case-specific methods, the main compound in a pulp and paper mill effluent causing RET inhibition was characterised to be an organic, relatively hydrophilic high molecular weight (HMW) compound. The toxicant could be verified as HMW lignin by structural analyses using nuclear magnetic resonance. In the confirmation step commercial and in-house extracted lignin products were used. The possible toxicity related structures were characterised by statistical analysis of the chemical breakdown structures of laboratory-scale pulping and bleaching effluents and the toxicities of these effluents. Finally, the biological degradation of the identified toxicant and other wastewater constituents was evaluated using bioassays in combination with chemical analyses. Biological methods have not been used routinely in establishing effluent discharge limits in Finland. However, the biological effects observed in this study could not have been predicted using only routine physical and chemical effluent monitoring parameters. Therefore chemical parameters cannot be considered to be sufficient in controlling effluent discharges especially in case of unknown, possibly bioaccumulative, compounds that may be present in small concentrations and may cause chronic effects.

Semantic Classes in Topic Detection and Tracking

Topic detection and tracking (TDT) is an area of information retrieval research the focus of which revolves around news events. The problems TDT deals with relate to segmenting news text into cohesive stories, detecting something new, previously unreported, tracking the development of a previously reported event, and grouping together news that discuss the same event. The performance of the traditional information retrieval techniques based on full-text similarity has remained inadequate for online production systems. It has been difficult to make the distinction between same and similar events. In this work, we explore ways of representing and comparing news documents in order to detect new events and track their development. First, however, we put forward a conceptual analysis of the notions of topic and event. The purpose is to clarify the terminology and align it with the process of news-making and the tradition of story-telling. Second, we present a framework for document similarity that is based on semantic classes, i.e., groups of words with similar meaning. We adopt people, organizations, and locations as semantic classes in addition to general terms. As each semantic class can be assigned its own similarity measure, document similarity can make use of ontologies, e.g., geographical taxonomies. The documents are compared class-wise, and the outcome is a weighted combination of class-wise similarities. Third, we incorporate temporal information into document similarity. We formalize the natural language temporal expressions occurring in the text, and use them to anchor the rest of the terms onto the time-line. Upon comparing documents for event-based similarity, we look not only at matching terms, but also how near their anchors are on the time-line. Fourth, we experiment with an adaptive variant of the semantic class similarity system. The news reflect changes in the real world, and in order to keep up, the system has to change its behavior based on the contents of the news stream. We put forward two strategies for rebuilding the topic representations and report experiment results. We run experiments with three annotated TDT corpora. The use of semantic classes increased the effectiveness of topic tracking by 10-30\% depending on the experimental setup. The gain in spotting new events remained lower, around 3-4\%. The anchoring the text to a time-line based on the temporal expressions gave a further 10\% increase the effectiveness of topic tracking. The gains in detecting new events, again, remained smaller. The adaptive systems did not improve the tracking results.

Measuring and tracking quality factors in Free and Open Source Software projects

Free and Open Source Software (FOSS) has gained increased interest in the computer software industry, but assessing its quality remains a challenge. FOSS development is frequently carried out by globally distributed development teams, and all stages of development are publicly visible. Several product and process-level quality factors can be measured using the public data. This thesis presents a theoretical background for software quality and metrics and their application in a FOSS environment. Information available from FOSS projects in three information spaces are presented, and a quality model suitable for use in a FOSS context is constructed. The model includes both process and product quality metrics, and takes into account the tools and working methods commonly used in FOSS projects. A subset of the constructed quality model is applied to three FOSS projects, highlighting both theoretical and practical concerns in implementing automatic metric collection and analysis. The experiment shows that useful quality information can be extracted from the vast amount of data available. In particular, projects vary in their growth rate, complexity, modularity and team structure.

Do Measurement Errors in GDP Announcements Cause Output Fluctuations?

This thesis studies empirically whether measurement errors in aggregate production statistics affect sentiment and future output. Initial announcements of aggregate production are subject to measurement error, because many of the data required to compile the statistics are produced with a lag. This measurement error can be gauged as the difference between the latest revised statistic and its initial announcement. Assuming aggregate production statistics help forecast future aggregate production, these measurement errors are expected to affect macroeconomic forecasts. Assuming agents’ macroeconomic forecasts affect their production choices, these measurement errors should affect future output through sentiment. This thesis is primarily empirical, so the theoretical basis, strategic complementarity, is discussed quite briefly. However, it is a model in which higher aggregate production increases each agent’s incentive to produce. In this circumstance a statistical announcement which suggests aggregate production is high would increase each agent’s incentive to produce, thus resulting in higher aggregate production. In this way the existence of strategic complementarity provides the theoretical basis for output fluctuations caused by measurement mistakes in aggregate production statistics. Previous empirical studies suggest that measurement errors in gross national product affect future aggregate production in the United States. Additionally it has been demonstrated that measurement errors in the Index of Leading Indicators affect forecasts by professional economists as well as future industrial production in the United States. This thesis aims to verify the applicability of these findings to other countries, as well as study the link between measurement errors in gross domestic product and sentiment. This thesis explores the relationship between measurement errors in gross domestic production and sentiment and future output. Professional forecasts and consumer sentiment in the United States and Finland, as well as producer sentiment in Finland, are used as the measures of sentiment. Using statistical techniques it is found that measurement errors in gross domestic product affect forecasts and producer sentiment. The effect on consumer sentiment is ambiguous. The relationship between measurement errors and future output is explored using data from Finland, United States, United Kingdom, New Zealand and Sweden. It is found that measurement errors have affected aggregate production or investment in Finland, United States, United Kingdom and Sweden. Specifically, it was found that overly optimistic statistics announcements are associated with higher output and vice versa.

Measurements of the top-quark mass using charged particle tracking

We present three measurements of the top-quark mass in the lepton plus jets channel with approximately 1.9 fb-1 of integrated luminosity collected with the CDF II detector using quantities with minimal dependence on the jet energy scale. One measurement exploits the transverse decay length of b-tagged jets to determine a top-quark mass of 166.9+9.5-8.5 (stat) +/- 2.9 (syst) GeV/c2, and another the transverse momentum of electrons and muons from W-boson decays to determine a top-quark mass of 173.5+8.8-8.9 (stat) +/- 3.8 (syst) GeV/c2. These quantities are combined in a third, simultaneous mass measurement to determine a top-quark mass of 170.7 +/- 6.3 (stat) +/- 2.6 (syst) GeV/c2.

Alignment of the ALICE Inner Tracking System with cosmic-ray tracks

ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 micron in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10^5 charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.

Measurements of the top-quark mass using charged particle tracking

We present three measurements of the top-quark mass in the lepton plus jets channel with approximately 1.9 fb-1 of integrated luminosity collected with the CDF II detector using quantities with minimal dependence on the jet energy scale. One measurement exploits the transverse decay length of b-tagged jets to determine a top-quark mass of 166.9+9.5-8.5 (stat) +/- 2.9 (syst) GeV/c2, and another the transverse momentum of electrons and muons from W-boson decays to determine a top-quark mass of 173.5+8.8-8.9 (stat) +/- 3.8 (syst) GeV/c2. These quantities are combined in a third, simultaneous mass measurement to determine a top-quark mass of 170.7 +/- 6.3 (stat) +/- 2.6 (syst) GeV/c2.

Investigating online reading with eye tracking and EEG : The influence of text format, reading task and parafoveal stimuli on reading processes

Research on reading has been successful in revealing how attention guides eye movements when people read single sentences or text paragraphs in simplified and strictly controlled experimental conditions. However, less is known about reading processes in more naturalistic and applied settings, such as reading Web pages. This thesis investigates online reading processes by recording participants eye movements. The thesis consists of four experimental studies that examine how location of stimuli presented outside the currently fixated region (Study I and III), text format (Study II), animation and abrupt onset of online advertisements (Study III), and phase of an online information search task (Study IV) affect written language processing. Furthermore, the studies investigate how the goal of the reading task affects attention allocation during reading by comparing reading for comprehension with free browsing, and by varying the difficulty of an information search task. The results show that text format affects the reading process, that is, vertical text (word/line) is read at a slower rate than a standard horizontal text, and the mean fixation durations are longer for vertical text than for horizontal text. Furthermore, animated online ads and abrupt ad onsets capture online readers attention and direct their gaze toward the ads, and distract the reading process. Compared to a reading-for-comprehension task, online ads are attended to more in a free browsing task. Moreover, in both tasks abrupt ad onsets result in rather immediate fixations toward the ads. This effect is enhanced when the ad is presented in the proximity of the text being read. In addition, the reading processes vary when Web users proceed in online information search tasks, for example when they are searching for a specific keyword, looking for an answer to a question, or trying to find a subjectively most interesting topic. A scanning type of behavior is typical at the beginning of the tasks, after which participants tend to switch to a more careful reading state before finishing the tasks in the states referred to as decision states. Furthermore, the results also provided evidence that left-to-right readers extract more parafoveal information to the right of the fixated word than to the left, suggesting that learning biases attentional orienting towards the reading direction.