Process Analytical Technology Approach on Fluid Bed Granulation and Drying : Identifying Critical Relationships and Constructing the Design Space

Fluid bed granulation is a key pharmaceutical process which improves many of the powder properties for tablet compression. Dry mixing, wetting and drying phases are included in the fluid bed granulation process. Granules of high quality can be obtained by understanding and controlling the critical process parameters by timely measurements. Physical process measurements and particle size data of a fluid bed granulator that are analysed in an integrated manner are included in process analytical technologies (PAT). Recent regulatory guidelines strongly encourage the pharmaceutical industry to apply scientific and risk management approaches to the development of a product and its manufacturing process. The aim of this study was to utilise PAT tools to increase the process understanding of fluid bed granulation and drying. Inlet air humidity levels and granulation liquid feed affect powder moisture during fluid bed granulation. Moisture influences on many process, granule and tablet qualities. The approach in this thesis was to identify sources of variation that are mainly related to moisture. The aim was to determine correlations and relationships, and utilise the PAT and design space concepts for the fluid bed granulation and drying. Monitoring the material behaviour in a fluidised bed has traditionally relied on the observational ability and experience of an operator. There has been a lack of good criteria for characterising material behaviour during spraying and drying phases, even though the entire performance of a process and end product quality are dependent on it. The granules were produced in an instrumented bench-scale Glatt WSG5 fluid bed granulator. The effect of inlet air humidity and granulation liquid feed on the temperature measurements at different locations of a fluid bed granulator system were determined. This revealed dynamic changes in the measurements and enabled finding the most optimal sites for process control. The moisture originating from the granulation liquid and inlet air affected the temperature of the mass and pressure difference over granules. Moreover, the effects of inlet air humidity and granulation liquid feed rate on granule size were evaluated and compensatory techniques used to optimize particle size. Various end-point indication techniques of drying were compared. The ∆T method, which is based on thermodynamic principles, eliminated the effects of humidity variations and resulted in the most precise estimation of the drying end-point. The influence of fluidisation behaviour on drying end-point detection was determined. The feasibility of the ∆T method and thus the similarities of end-point moisture contents were found to be dependent on the variation in fluidisation between manufacturing batches. A novel parameter that describes behaviour of material in a fluid bed was developed. Flow rate of the process air and turbine fan speed were used to calculate this parameter and it was compared to the fluidisation behaviour and the particle size results. The design space process trajectories for smooth fluidisation based on the fluidisation parameters were determined. With this design space it is possible to avoid excessive fluidisation and improper fluidisation and bed collapse. Furthermore, various process phenomena and failure modes were observed with the in-line particle size analyser. Both rapid increase and a decrease in granule size could be monitored in a timely manner. The fluidisation parameter and the pressure difference over filters were also discovered to express particle size when the granules had been formed. The various physical parameters evaluated in this thesis give valuable information of fluid bed process performance and increase the process understanding.

Bayesian designs with frequentist and Bayesian error rate considerations

So far, most Phase II trials have been designed and analysed under a frequentist framework. Under this framework, a trial is designed so that the overall Type I and Type II errors of the trial are controlled at some desired levels. Recently, a number of articles have advocated the use of Bavesian designs in practice. Under a Bayesian framework, a trial is designed so that the trial stops when the posterior probability of treatment is within certain prespecified thresholds. In this article, we argue that trials under a Bayesian framework can also be designed to control frequentist error rates. We introduce a Bayesian version of Simon's well-known two-stage design to achieve this goal. We also consider two other errors, which are called Bayesian errors in this article because of their similarities to posterior probabilities. We show that our method can also control these Bayesian-type errors. We compare our method with other recent Bayesian designs in a numerical study and discuss implications of different designs on error rates. An example of a clinical trial for patients with nasopharyngeal carcinoma is used to illustrate differences of the different designs.

Univariate, bivariate, and multivariate methods in corpus-based lexicography : A study of synonymy

In this dissertation, I present an overall methodological framework for studying linguistic alternations, focusing specifically on lexical variation in denoting a single meaning, that is, synonymy. As the practical example, I employ the synonymous set of the four most common Finnish verbs denoting THINK, namely ajatella, miettiä, pohtia and harkita ‘think, reflect, ponder, consider’. As a continuation to previous work, I describe in considerable detail the extension of statistical methods from dichotomous linguistic settings (e.g., Gries 2003; Bresnan et al. 2007) to polytomous ones, that is, concerning more than two possible alternative outcomes. The applied statistical methods are arranged into a succession of stages with increasing complexity, proceeding from univariate via bivariate to multivariate techniques in the end. As the central multivariate method, I argue for the use of polytomous logistic regression and demonstrate its practical implementation to the studied phenomenon, thus extending the work by Bresnan et al. (2007), who applied simple (binary) logistic regression to a dichotomous structural alternation in English. The results of the various statistical analyses confirm that a wide range of contextual features across different categories are indeed associated with the use and selection of the selected think lexemes; however, a substantial part of these features are not exemplified in current Finnish lexicographical descriptions. The multivariate analysis results indicate that the semantic classifications of syntactic argument types are on the average the most distinctive feature category, followed by overall semantic characterizations of the verb chains, and then syntactic argument types alone, with morphological features pertaining to the verb chain and extra-linguistic features relegated to the last position. In terms of overall performance of the multivariate analysis and modeling, the prediction accuracy seems to reach a ceiling at a Recall rate of roughly two-thirds of the sentences in the research corpus. The analysis of these results suggests a limit to what can be explained and determined within the immediate sentential context and applying the conventional descriptive and analytical apparatus based on currently available linguistic theories and models. The results also support Bresnan’s (2007) and others’ (e.g., Bod et al. 2003) probabilistic view of the relationship between linguistic usage and the underlying linguistic system, in which only a minority of linguistic choices are categorical, given the known context – represented as a feature cluster – that can be analytically grasped and identified. Instead, most contexts exhibit degrees of variation as to their outcomes, resulting in proportionate choices over longer stretches of usage in texts or speech.

A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation.

The development of innovative methods of stock assessment is a priority for State and Commonwealth fisheries agencies. It is driven by the need to facilitate sustainable exploitation of naturally occurring fisheries resources for the current and future economic, social and environmental well being of Australia. This project was initiated in this context and took advantage of considerable recent achievements in genomics that are shaping our comprehension of the DNA of humans and animals. The basic idea behind this project was that genetic estimates of effective population size, which can be made from empirical measurements of genetic drift, were equivalent to estimates of the successful number of spawners that is an important parameter in process of fisheries stock assessment. The broad objectives of this study were to 1. Critically evaluate a variety of mathematical methods of calculating effective spawner numbers (Ne) by a. conducting comprehensive computer simulations, and by b. analysis of empirical data collected from the Moreton Bay population of tiger prawns (P. esculentus). 2. Lay the groundwork for the application of the technology in the northern prawn fishery (NPF). 3. Produce software for the calculation of Ne, and to make it widely available. The project pulled together a range of mathematical models for estimating current effective population size from diverse sources. Some of them had been recently implemented with the latest statistical methods (eg. Bayesian framework Berthier, Beaumont et al. 2002), while others had lower profiles (eg. Pudovkin, Zaykin et al. 1996; Rousset and Raymond 1995). Computer code and later software with a user-friendly interface (NeEstimator) was produced to implement the methods. This was used as a basis for simulation experiments to evaluate the performance of the methods with an individual-based model of a prawn population. Following the guidelines suggested by computer simulations, the tiger prawn population in Moreton Bay (south-east Queensland) was sampled for genetic analysis with eight microsatellite loci in three successive spring spawning seasons in 2001, 2002 and 2003. As predicted by the simulations, the estimates had non-infinite upper confidence limits, which is a major achievement for the application of the method to a naturally-occurring, short generation, highly fecund invertebrate species. The genetic estimate of the number of successful spawners was around 1000 individuals in two consecutive years. This contrasts with about 500,000 prawns participating in spawning. It is not possible to distinguish successful from non-successful spawners so we suggest a high level of protection for the entire spawning population. We interpret the difference in numbers between successful and non-successful spawners as a large variation in the number of offspring per family that survive – a large number of families have no surviving offspring, while a few have a large number. We explored various ways in which Ne can be useful in fisheries management. It can be a surrogate for spawning population size, assuming the ratio between Ne and spawning population size has been previously calculated for that species. Alternatively, it can be a surrogate for recruitment, again assuming that the ratio between Ne and recruitment has been previously determined. The number of species that can be analysed in this way, however, is likely to be small because of species-specific life history requirements that need to be satisfied for accuracy. The most universal approach would be to integrate Ne with spawning stock-recruitment models, so that these models are more accurate when applied to fisheries populations. A pathway to achieve this was established in this project, which we predict will significantly improve fisheries sustainability in the future. Regardless of the success of integrating Ne into spawning stock-recruitment models, Ne could be used as a fisheries monitoring tool. Declines in spawning stock size or increases in natural or harvest mortality would be reflected by a decline in Ne. This would be good for data-poor fisheries and provides fishery independent information, however, we suggest a species-by-species approach. Some species may be too numerous or experiencing too much migration for the method to work. During the project two important theoretical studies of the simultaneous estimation of effective population size and migration were published (Vitalis and Couvet 2001b; Wang and Whitlock 2003). These methods, combined with collection of preliminary genetic data from the tiger prawn population in southern Gulf of Carpentaria population and a computer simulation study that evaluated the effect of differing reproductive strategies on genetic estimates, suggest that this technology could make an important contribution to the stock assessment process in the northern prawn fishery (NPF). Advances in the genomics world are rapid and already a cheaper, more reliable substitute for microsatellite loci in this technology is available. Digital data from single nucleotide polymorphisms (SNPs) are likely to super cede ‘analogue’ microsatellite data, making it cheaper and easier to apply the method to species with large population sizes.

How can warren destruction by ripping control European wild rabbits (Oryctolagus cuniculus) on large properties in the Australian arid zone?

Context: For over 100 years, control efforts have been unable to stop rabbits causing damage to cattle production and native plants and animals on large properties in arid parts of Australia. Warren destruction by ripping has shown promise, but doubts about long-term success and the perceived expense of treating vast areas have led to this technique not being commonly used. Aims: This study measured the long-term reduction in rabbit activity and calculated the potential cost saving associated with treating just the areas where rabbits are believed to survive drought. Wealso considered whether ripping should be used in a full-scale rabbit control program on a property where rabbits have been exceptionally resilient to the influence of biological and other control measures. Methods: Rabbits were counted along spotlight transects before warrens were ripped and during the two years after ripping, in treated and untreated plots. Rabbit activity was recorded to determine the immediate and long-term impact of ripping, up to seven years after treatment. The costs of ripping warrens within different distances from drought refuge areas were calculated. Key results: Destroying rabbit warrens by ripping caused an immediate reduction in rabbit activity and there were still 98% fewer rabbits counted by spotlight in ripped plots five months after ripping. Seven years after ripping no active warrens were found in ripped plots, whereas 57% of warrens in unripped plots showed signs of rabbit activity. The cost of ripping only the areas where rabbits were likely to seek refuge from drought was calculated to be less than 4%of the cost of ripping all warrens on the property. Conclusions: Destroying rabbit warrens by ripping is a very effective way of reducing rabbit numbers on large properties in arid Australia. Ripping should commence in areas used by rabbits to survive drought. It is possible that no further ripping will be required. Implications: Strategic destruction of warrens in drought refuge areas could provide an alternative to biological control for managing rabbits on large properties in the Australian arid zone.

Information, risk and trust in the food chain : Ex-ante valuation of consumer willingness to pay for beef quality information using the contingent valuation method

The safety of food has become an increasingly interesting issue to consumers and the media. It has also become a source of concern, as the amount of information on the risks related to food safety continues to expand. Today, risk and safety are permanent elements within the concept of food quality. Safety, in particular, is the attribute that consumers find very difficult to assess. The literature in this study consists of three main themes: traceability; consumer behaviour related to both quality and safety issues and perception of risk; and valuation methods. The empirical scope of the study was restricted to beef, because the beef labelling system enables reliable tracing of the origin of beef, as well as attributes related to safety, environmental friendliness and animal welfare. The purpose of this study was to examine what kind of information flows are required to ensure quality and safety in the food chain for beef, and who should produce that information. Studying the willingness to pay of consumers makes it possible to determine whether the consumers consider the quantity of information available on the safety and quality of beef sufficient. One of the main findings of this study was that the majority of Finnish consumers (73%) regard increased quality information as beneficial. These benefits were assessed using the contingent valuation method. The results showed that those who were willing to pay for increased information on the quality and safety of beef would accept an average price increase of 24% per kilogram. The results showed that certain risk factors impact consumer willingness to pay. If the respondents considered genetic modification of food or foodborne zoonotic diseases as harmful or extremely harmful risk factors in food, they were more likely to be willing to pay for quality information. The results produced by the models thus confirmed the premise that certain food-related risks affect willingness to pay for beef quality information. The results also showed that safety-related quality cues are significant to the consumers. In the first place, the consumers would like to receive information on the control of zoonotic diseases that are contagious to humans. Similarly, other process-control related information ranked high among the top responses. Information on any potential genetic modification was also considered important, even though genetic modification was not regarded as a high risk factor.

Environmental factors affecting the occurrence of periglacial landforms in Finnish Lapland: a numerical approach

Determination of the environmental factors controlling earth surface processes and landform patterns is one of the central themes in physical geography. However, the identification of the main drivers of the geomorphological phenomena is often challenging. Novel spatial analysis and modelling methods could provide new insights into the process-environment relationships. The objective of this research was to map and quantitatively analyse the occurrence of cryogenic phenomena in subarctic Finland. More precisely, utilising a grid-based approach the distribution and abundance of periglacial landforms were modelled to identify important landscape scale environmental factors. The study was performed using a comprehensive empirical data set of periglacial landforms from an area of 600 km2 at a 25-ha resolution. The utilised statistical methods were generalized linear modelling (GLM) and hierarchical partitioning (HP). GLMs were used to produce distribution and abundance models and HP to reveal independently the most likely causal variables. The GLM models were assessed utilising statistical evaluation measures, prediction maps, field observations and the results of HP analyses. A total of 40 different landform types and subtypes were identified. Topographical, soil property and vegetation variables were the primary correlates for the occurrence and cover of active periglacial landforms on the landscape scale. In the model evaluation, most of the GLMs were shown to be robust although the explanation power, prediction ability as well as the selected explanatory variables varied between the models. The great potential of the combination of a spatial grid system, terrain data and novel statistical techniques to map the occurrence of periglacial landforms was demonstrated in this study. GLM proved to be a useful modelling framework for testing the shapes of the response functions and significances of the environmental variables and the HP method helped to make better deductions of the important factors of earth surface processes. Hence, the numerical approach presented in this study can be a useful addition to the current range of techniques available to researchers to map and monitor different geographical phenomena.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

A deadline-constrained 802.11 MAC Protocol with QoS differentiation for soft real-time control

As one of the most widely used wireless network technologies, IEEE 802.11 wireless local area networks (WLANs) have found a dramatically increasing number of applications in soft real-time networked control systems (NCSs). To fulfill the real-time requirements in such NCSs, most of the bandwidth of the wireless networks need to be allocated to high-priority data for periodic measurements and control with deadline requirements. However, existing QoS-enabled 802.11 medium access control (MAC) protocols do not consider the deadline requirements explicitly, leading to unpredictable deadline performance of NCS networks. Consequentially, the soft real-time requirements of the periodic traffic may not be satisfied, particularly under congested network conditions. This paper makes two main contributions to address this problem in wireless NCSs. Firstly, a deadline-constrained MAC protocol with QoS differentiation is presented for IEEE 802.11 soft real-time NCSs. It handles periodic traffic by developing two specific mechanisms: a contention-sensitive backoff mechanism, and an intra-traffic-class QoS differentiation mechanism. Secondly, a theoretical model is established to describe the deadline-constrained MAC protocol and evaluate its performance of throughput, delay and packet-loss ratio in wireless NCSs. Numerical studies are conducted to validate the accuracy of the theoretical model and to demonstrate the effectiveness of the new MAC protocol.

Microbial communities in Pb contaminated boreal forest soil

Lead contamination in the environment is of particular concern, as it is a known toxin. Until recently, however, much less attention has been given to the local contamination caused by activities at shooting ranges compared to large-scale industrial contamination. In Finland, more than 500 tons of Pb is produced each year for shotgun ammunition. The contaminant threatens various organisms, ground water and the health of human populations. However, the forest at shooting ranges usually shows no visible sign of stress compared to nearby clean environments. The aboveground biota normally reflects the belowground ecosystem. Thus, the soil microbial communities appear to bear strong resistance to contamination, despite the influence of lead. The studies forming this thesis investigated a shooting range site at Hälvälä in Southern Finland, which is heavily contaminated by lead pellets. Previously it was experimentally shown that the growth of grasses and degradation of litter are retarded. Measurements of acute toxicity of the contaminated soil or soil extracts gave conflicting results, as enchytraeid worms used as toxicity reporters were strongly affected, while reporter bacteria showed no or very minor decreases in viability. Measurements using sensitive inducible luminescent reporter bacteria suggested that the bioavailability of lead in the soil is indeed low, and this notion was supported by the very low water extractability of the lead. Nevertheless, the frequency of lead-resistant cultivable bacteria was elevated based on the isolation of cultivable strains. The bacterial and fungal diversity in heavily lead contaminated shooting sectors were compared with those of pristine sections of the shooting range area. The bacterial 16S rRNA gene and fungal ITS rRNA gene were amplified, cloned and sequenced using total DNA extracted from the soil humus layer as the template. Altogether, 917 sequenced bacterial clones and 649 sequenced fungal clones revealed a high soil microbial diversity. No effect of lead contamination was found on bacterial richness or diversity, while fungal richness and diversity significantly differed between lead contaminated and clean control areas. However, even in the case of fungi, genera that were deemed sensitive were not totally absent from the contaminated area: only their relative frequency was significantly reduced. Some operational taxonomic units (OTUs) assigned to Basidiomycota were clearly affected, and were much rarer in the lead contaminated areas. The studies of this thesis surveyed EcM sporocarps, analyzed morphotyped EcM root tips by direct sequencing, and 454-pyrosequenced fungal communities in in-growth bags. A total of 32 EcM fungi that formed conspicuous sporocarps, 27 EcM fungal OTUs from 294 root tips, and 116 EcM fungal OTUs from a total of 8 194 ITS2 454 sequences were recorded. The ordination analyses by non-parametric multidimensional scaling (NMS) indicated that Pb enrichment induced a shift in the EcM community composition. This was visible as indicative trends in the sporocarp and root tip datasets, but explicitly clear in the communities observed in the in-growth bags. The compositional shift in the EcM community was mainly attributable to an increase in the frequencies of OTUs assigned to the genus Thelephora, and to a decrease in the OTUs assigned to Pseudotomentella, Suillus and Tylospora in Pb-contaminated areas when compared to the control. The enrichment of Thelephora in contaminated areas was also observed when examining the total fungal communities in soil using DNA cloning and sequencing technology. While the compositional shifts are clear, their functional consequences for the dominant trees or soil ecosystem remain undetermined. The results indicate that at the Hälvälä shooting range, lead influences the fungal communities but not the bacterial communities. The forest ecosystem shows apparent functional redundancy, since no significant effects were seen on forest trees. Recently, by means of 454 pyrosequencing , the amount of sequences in a single analysis run can be up to one million. It has been applied in microbial ecology studies to characterize microbial communities. The handling of sequence data with traditional programs is becoming difficult and exceedingly time consuming, and novel tools are needed to handle the vast amounts of data being generated. The field of microbial ecology has recently benefited from the availability of a number of tools for describing and comparing microbial communities using robust statistical methods. However, although these programs provide methods for rapid calculation, it has become necessary to make them more amenable to larger datasets and numbers of samples from pyrosequencing. As part of this thesis, a new program was developed, MuSSA (Multi-Sample Sequence Analyser), to handle sequence data from novel high-throughput sequencing approaches in microbial community analyses. The greatest advantage of the program is that large volumes of sequence data can be manipulated, and general OTU series with a frequency value can be calculated among a large number of samples.

Detecting drift from event streams of unpredictable business processes

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Why Don't Students Talk? Causes of Foreign Language Communication Apprehension in English Classes: A Comparison of High Apprehensive and Low Apprehensive Upper Secondary Students.

OBJECTIVES. Oral foreign language skills are an integral part of one's social, academic and professional competence. This can be problematic for those suffering from foreign language communication apprehension (CA), or a fear of speaking a foreign language. CA manifests itself, for example, through feelings of anxiety and tension, physical arousal and avoidance of foreign language communication situations. According to scholars, foreign language CA may impede the language learning process significantly and have detrimental effects on one's language learning, academic achievement and career prospects. Drawing on upper secondary students' subjective experiences of communication situations in English as a foreign language, this study seeks, first, to describe, analyze and interpret why upper secondary students experience English language communication apprehension in English as a foreign language (EFL) classes. Second, this study seeks to analyse what the most anxiety-arousing oral production tasks in EFL classes are, and which features of different oral production tasks arouse English language communication apprehension and why. The ultimate objectives of the present study are to raise teachers' awareness of foreign language CA and its features, manifestations and impacts in foreign language classes as well as to suggest possible ways to minimize the anxiety-arousing features in foreign language classes. METHODS. The data was collected in two phases by means of six-part Likert-type questionnaires and theme interviews, and analysed using both quantitative and qualitative methods. The questionnaire data was collected in spring 2008. The respondents were 122 first-year upper secondary students, 68 % of whom were girls and 31 % of whom were boys. The data was analysed by statistical methods using SPSS software. The theme interviews were conducted in spring 2009. The interviewees were 11 second-year upper secondary students aged 17 to 19, who were chosen by purposeful selection on the basis of their English language CA level measured in the questionnaires. Six interviewees were classified as high apprehensives and five as low apprehensives according to their score in the foreign language CA scale in the questionnaires. The interview data was coded and thematized using the technique of content analysis. The analysis and interpretation of the data drew on a comparison of the self-reports of the highly apprehensive and low apprehensive upper secondary students. RESULTS. The causes of English language CA in EFL classes as reported by the students were both internal and external in nature. The most notable causes were a low self-assessed English proficiency, a concern over errors, a concern over evaluation, and a concern over the impression made on others. Other causes related to a high English language CA were a lack of authentic oral practise in EFL classes, discouraging teachers and negative experiences of learning English, unrealistic internal demands for oral English performance, high external demands and expectations for oral English performance, the conversation partner's higher English proficiency, and the audience's large size and unfamiliarity. The most anxiety-arousing oral production tasks in EFL classes were presentations or speeches with or without notes in front of the class, acting in front of the class, pair debates with the class as audience, expressing thoughts and ideas to the class, presentations or speeches without notes while seated, group debates with the class as audience, and answering to the teacher's questions involuntarily. The main features affecting the anxiety-arousing potential of an oral production task were a high degree of attention, a large audience, a high degree of evaluation, little time for preparation, little linguistic support, and a long duration.

Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.