Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactions between canopy forming algae in the eulittoral zone of sheltered rocky shores on the Isle of Man

The distribution and abundance of Ascophyllum nodosum, Fucus serratus and F. vesiculosus were described at four sheltered, rocky shores in the south of the Isle of Man. Canopy removal experiments were performed at mid tide level of one sheltered, canopy dominated shore to investigate the interactions between the dominant canopy alga, Ascophyllum nodosum and the competitively inferior canopy species of Fucus serratus and F. vesiculosus. Ascophyllum was removed from replicated plots, 2×2 m in size, in both winter and summer; the early growth and survival of fucoids in the presence and absence of the Ascophyllum canopy were monitored and the eventual development of a new canopy described. Juveniles of F. serratus originally present beneath the undisturbed canopy of Ascophyllum died following canopy removal but new recruitment resulted in some canopy development, principally in the winter experiment. Fucus vesiculosus, despite being completely excluded from the Ascophyllum zone of all four shores described, dominated the canopy removal plots of both winter and summer experiments. The Ascophyllum canopy did not recover over a five year period of observation, although a considerable increase in the abundance of Ascophyllum juveniles occurred.
Enzymatic and Non-enzymatic Antioxidant and Lipid Peroxidation Status in Iron Deficient Anemic Individuals

The objective of this study was to assess the enzymatic and non-enzymatic antioxidant levels in patients with iron-deficiency anemia (IDA). Forty patients with IDA and forty healthy individuals with their informed consent were selected for the study. The enzymatic antioxidants-catalase, glutathione peroxidase and superoxide dismutase; non-enzymatic antioxidants-vitamin C and E and total antioxidant activity (TAA) were also analyzed. The lipid peroxidation parameter thiobarbituric acid reactive substance was analyzed. The enzymatically antioxidants were found to be statistically increased but vitamin C and E and TAA were significantly lowered. Diminished antioxidant status disturbed oxidant-antioxidant balance alleviating oxidative stress state.
The Impact of Regulation and Economic Conditions on the Dynamics of Financial Markets

This dissertation encompasses four studies on selected topics in financial regulation and financial stability. The first paper asks whether there is empirical evidence of cyclicality in regulatory capital requirements prescribed by Basel regimes. This much debated issue was until then only addressed in theoretical papers, or simulation studies. While we do not find evidence on cyclicality in the Basel I or Basel II Standardized Approach, we find statistically and economically significant evidence concerning Basel II IRB portfolios. The second paper implements an agent based model to simulate an artificial asset market. This setup is then used to assess the impact of (i) a short selling ban, (ii) a Tobin Tax like transaction tax, (iii) mandatory Value-at-Risk limits and (iv) arbitrary combinations of these. I present results that show that while reducing volatility, a short selling ban nurtures market bubbles, and a Tobin Tax increases the variance of the returns. In this model a mandatory risk limit is beneficial from all stability perspectives taken. I examine the robustness of the model regarding its initial parameterization and show that high levels of a Tobin Tax lead to substantial market turbulence. The third paper considers the question which macroeconomic variables are linked to a time series of special interest from a financial stability perspective: firm defaults. Furthermore, we evaluate the empirical evidence of a hidden credit cycle by adding a latent factor to our models. We conclude that there is no empirical support of a hidden credit cycle in Austria once sufficient regressors are included and industry sectors differ in their respective macro drivers. The forth paper extends this work by implementing Bayesian Model Averaging (BMA) - a modern technique to counter model uncertainty. Furthermore we enrich this statistical approach by combining BMA with Bayesian ridge regression. We draw the conclusion that BMA is indeed a powerful tool to counter model uncertainty. Interest rates and components of inflation are distilled as major drivers for firm failures in Austria. (author's abstract)
Event Extraction from Heterogeneous News Sources

With the proliferation of news articles from thousands of different sources now available on the Web, summarization of such information is becoming increasingly important. Our research focuses on merging descriptions of news events from multiple sources, to provide a concise description that combines the information from each source. Specifically, we describe and evaluate methods for grouping sentences in news articles that refer to the same event. The key idea is to cluster the sentences, using two novel distance metrics. The first distance metric exploits regularities in the sequential structure of events within a document. The second metric uses a TFIDF-like weighting scheme, enhanced to capture word frequencies within events even though the events themselves are not known a priori. Typical news articles contain sentences that do not describe specific events. We use machine learning methods to differentiate between sentences that describe one or more events, and those that do not. We then remove non-event sentences before initiating the clustering process. We demonstrate that this approach achieves significant improvements in overall clustering performance.
New insights into the regulatory mechanisms of ppGpp and DksA on Escherichia coli RNA polymerase–promoter complex

The stringent response modulators, guanosine tetraphosphate (ppGpp) and protein DksA, bind RNA polymerase (RNAP) and regulate gene expression to adapt bacteria to different environmental conditions. Here, we use Atomic Force Microscopy and in vitro transcription assays to study the effects of these modulators on the conformation and stability of the open promoter complex (RPo) formed at the rrnA P1, rrnB P1, its discriminator (dis) variant and λ pR promoters. In the absence of modulators, RPo formed at these promoters show different extents of DNA wrapping which correlate with the position of UP elements. Addition of the modulators affects both DNA wrapping and RPo stability in a promoter-dependent manner. Overall, the results obtained under different conditions of ppGpp, DksA and initiating nucleotides (iNTPs) indicate that ppGpp allosterically prevents the conformational changes associated with an extended DNA wrapping that leads to RPo stabilization, while DksA interferes directly with nucleotide positioning into the RNAP active site. At the iNTPs-sensitive rRNA promoters ppGpp and DksA display an independent inhibitory effect, while at the iNTPs-insensitive pR promoter DksA reduces the effect of ppGpp in accordance with their antagonistic role.
Page Dissemination Modeling and Optimization for Coding-Based Reprogramming Protocols in Wireless Sensor Networks

Coding-based reprogramming protocols are presented to realize efficient over-the-air reprogramming in wireless sensor networks with high packet loss rate. However, existing mathematical analysis models for these protocols have considerable deviation with real data. In this paper, we present a novel page dissemination time model to reduce the deviation. Our model comprehensively analyzes the details of the protocols, handling the impacts on page encoding, page decoding, page pipelining and negotiation mechanism. It can be used to aid protocol design for performance optimizations. For minimizing the time of page dissemination, we present a Firefly Algorithm with feasibility-based rules to search an optimal solution (i.e., the number of data packets per page). Results from the simulations of a representative coding-based reprogramming protocol as Rateless Deluge are in good accordance with the performance forecasted by our model, thus verifying the validity of our model. Moreover, our Firefly Algorithm can converge to the global optimal solution quickly and efficiently.
CFD and Artificial Neural Networks Analysis of Plane Sudden Expansion Flows

It has been clearly established that the reattachment length for laminar flow depends on two non-dimensional parameters, the Reynolds number and the expansion ratio, therefore in this work, an ANN model that predict reattachment positions for the expansion ratios of 2, 3 and 5 based on the above two parameters has been developed. The R values of the testing set output Xr1, Xr2, Xr3, and Xr4 were 0.9383, 0.8577, 0.997 and 0.999 respectively. These results indicate that the network model produced reattachment positions that were in close agreement with the actual values. When considering the reattachment length of plane sudden-expansions the judicious combination of CFD calculated solutions with ANN will result in a considerable saving in computing and turnaround time. Thus CFD can be used in the first instance to obtain reattachment lengths for a limited choice of Reynolds numbers and ANN will be used subsequently to predict the reattachment lengths for other intermediate Reynolds number values. The CFD calculations concern unsteady laminar flow through a plane sudden expansion and are performed using a commercial CFD code STAR-CD while the training process of the corresponding ANN model was performed using the NeuroShellTM simulator.
Saussurian analogy: a theoretical account and its application

In the Cours de linguistique generale, Saussure mentions a phenomenon of tremendous importance in language, analogy. For example, given the series walk, walked and look, how can we coin the fourth term, looked? We give a possible account of this phenomenon in terms of edition distances, thus paving the way to computational applications. This explanation accounts for prefixing, suffixing and infixing. We show how it is possible to perform the analogical analysis and generation of sentences, using a tree-bank and approximate pattern-matching. As a consequence, our proposal finds its place in the example-based approach to natural language processing.
High Connectivity among Blue Crab (Callinectes sapidus) Populations in the Western South Atlantic

Population connectivity in the blue crab Callinectes sapidus was evaluated along 740 km of the Western South Atlantic coast. Blue crabs are the most exploited portunid in Brazil. Despite their economic importance, few studies report their ecology or population structure. Here we sampled four estuarine areas in southern Brazil during winter 2013 and summer 2014 in order to evaluate diversity, gene flow and structure of these populations. Nine microsatellite markers were evaluated for 213 adult crabs, with identification of seven polymorphic loci and 183 alleles. Pairwise FST values indicated low population structure ranging from -0.00023 to 0.01755. A Mantel test revealed that the geographic distance does not influence genetic (r = -0.48), and structure/migration rates confirmed this, showing that even the populations located at the opposite extremities of our covered region presented low FST and exchanged migrants. These findings show that there is a significant amount of gene flow between blue crab populations in South Brazil, likely influenced by local current dynamics that allow the transport of a high number of larvae between estuaries. Considering the elevated gene flow, the populations can be considered a single genetic stock. However, further information on population size and dynamics, as well as fishery demands and impacts at different regions, are necessary for harvest management purposes.
Research Methods in Child Welfare

List of Figures List of Tables Foreword, by Christine James-Brown Acknowledgments 1. Introduction to Child Welfare Research Part 1. Planning and Developing Research Studies 2. The Philosophy and Logic of Research 3. Ethical Considerations 4. Problem Formulation 5. Sampling Theory Part 2. Design Strategies 6. Single-System Design 7. Group Designs 8. Using Existing Data Part 3. Measurement Strategies 9. Measurement Theory and Measure Selection 10. Surveys 11. Case Studies 12. Focus Groups 13. In-Depth Interviews Part 4. Data Analysis and Writing Up and Sharing Research Results 14. Data Analysis Techniques 15. Dissemination of Research Findings Part 5. Special Topics in Agency Practice 16. Continuous Quality Improvement in Agency Practice 17. Agency-Based Program Evaluation 18. Final Thoughts Classroom Discussion and Activities Glossary 1. Social Science Research Methods Terms Glossary 2. Child Welfare Practice and Policy Terms References Index
Product review sentiment classification using parts of speech

A prospective buyer interested in a particular item may find out information about the item from various sources, including product reviews. With interactive information sharing facilitated by Web 2.0, a lot of product reviews are available on the web. For a popular item with a large number of reviews, a prospective buyer could use some help in selecting only reviews of interest, such as, only positive or negative reviews, when only particular kind of information is being sought for. This research work implemented a system that classified a product review as having either positive or negative tone, through the analysis of parts of speech of the review's textual content. The system used machine learning algorithms for training positive-negative classification models. Experiments were performed particularly on textbook reviews.
Virtual Currencies Like Bitcoin As A Paradigm Shift In The Field Of Transactions

Virtual currencies have been well-cited and well-discussed in the near past. Due to the loss of trust in the banking sector and the fear of loss of capital, low interest rates and uncertainty of existing currencies, the ground for a virtual currency was given. Virtual currencies and the money flows are controlled only online by the anonymous group of volunteers (also called peer); every single transaction is documented. Approximately 10,000 businesses worldwide accept payments with virtual currencies already, and the number is increasing steadily. This article analyzes the advantages and disadvantages of virtual currencies in comparison to real money and gives an outlook to a new banking system with high transparency and the chance to lead to a paradigm shift in the world of transactions and banking.
It’s a bug’s life: biosensors for environmental monitoring

As we strive to better understand and manage risks to human health and ecosystems, the demands on environmental monitoring are increasing. Stricter regulations and a greater public awareness of environmental issues bring requirements to monitor an ever-wider range of analytes in air, water and soil, and to do so with greater frequency and accuracy. Meanwhile, operators are looking to contain the costs of increasingly complex monitoring regimes. In response to these issues, environmental analysts have sought improvements in laboratorybased analytical methods as well as portable solutions that allow sampling and analysis to be undertaken reliably on-site. Analysts already have a range of portable analytical techniques at their disposal for continuous, in situ monitoring across a variety of environmental matrices and analytes. In the 1970s, portable gas chromatographs were developed that brought smaller, more rugged versions of laboratory models to newly-identified hazardous waste sites. These were followed in the 1980s by immunochemical techniques – inspired by clinical laboratories – that were capable of tackling an everincreasing list of environmental analytes. More recently, biosensors has emerged as another promising technology in the analyst’s armoury, especially for applications requiring continuous monitoring. But what are biosensors exactly and how will they contribute to environmental monitoring and analysis?
Beyond Parkinson Disease: Amyotrophic Lateral Sclerosis and the Axon Guidance Pathway

Background We recently described a genomic pathway approach to study complex diseases. We demonstrated that models constructed using single nucleotide polymorphisms (SNPs) within axon guidance pathway genes were highly predictive of Parkinson disease (PD) susceptibility, survival free of PD, and age at onset of PD within two independent whole-genome association datasets. We also demonstrated that several axon guidance pathway genes represented by SNPs within our final models were differentially expressed in PD. Methodology/Principal Findings Here we employed our genomic pathway approach to analyze data from a whole-genome association dataset of amyotrophic lateral sclerosis (ALS); and demonstrated that models constructed using SNPs within axon guidance pathway genes were highly predictive of ALS susceptibility (odds ratio = 1739.73, p = 2.92×10−60), survival free of ALS (hazards ratio = 149.80, p = 1.25×10−74), and age at onset of ALS (R2 = 0.86, p = 5.96×10−66). We also extended our analyses of a whole-genome association dataset of PD, which shared 320,202 genomic SNPs in common with the whole-genome association dataset of ALS. We compared for ALS and PD the genes represented by SNPs in the final models for susceptibility, survival free of disease, and age at onset of disease and noted that 52.2%, 37.8%, and 34.9% of the genes were shared respectively. Conclusions/Significance Our findings for the axon guidance pathway and ALS have prior biological plausibility, overlap partially with PD, and may provide important insight into the causes of these and related neurodegenerative disorders.
Correctness and Completeness of Generalised Concurrent Model Synchronisation Based on Triple Graph Grammars

Triple graph grammars (TGGs) have been applied successfully for specifying and analysing bidirectional model transformations. Recently, a formal approach to concurrent model synchronisation has been presented, where source and target modifications are synchronised simultaneously. In addition to methods for update propagation, the approach includes a semi-automatic strategy for conflict resolution. Up to now, this approach has been limited to deterministic propagation operations. In this paper, we generalise the approach to arbitrary TGGs and consider nondeterministic operations which might yield different results and require backtracking. We show correctness and completeness of the extended approach and provide a technique for reducing and possibly eliminating backtracking to improve efficiency.
Fetal ECG Extraction Using Adaptive Filters

Fetal electrocardiogram (FECG) extraction has a vital role in medical diagnosis during pregnancy. In this paper, a method for removal of background noise and artifacts from FECG signals using adaptive filters is proposed. The proposed method uses adaptive noise cancellation and digital filters for FECG extraction. Proposed FECG extraction algorithm is implemented in MATLAB using simulink models. Simulation results shows that fetal heart rate can be extracted by counting the peaks of R-R interval from the extracted FECG noise free signal.
Accelerated image reconstruction using ordered subsets of projection data

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained.
Structural Econometric Modeling: Rationales and Examples from Industrial Organization

This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial organization economics, including the literatures dealing with market power, product differentiation, auctions, regulation and entry.
Activation Points Extraction and Noise Removal of fMRI Signal

In this paper we report a novel procedure to accurately estimate the power spectrum of the noise in the fMRI signal at a given voxel location; the estimated power spectrum is used to determine the threshold used as shrinkage or soft threshold to remove noise from both 1-D and 2-D fMRI signal. Spatial processing, such as clustering is done on the entire signal to isolate the BOLD response and further investigate whether the new positions and numbers of the activation points are different from that of theoretically anticipated positions for the experiment performed. It is confirmed that the anticipated positions of the processed fMRI data and the actual positions of the activation points of the original fMRI data coincide as expected theoretically for the experiment performed.
A Bayesian Model Averaging Methodology for Detecting EEG Artifacts

In this paper we describe a Bayesian Model Averaging (BMA) methodology developed for detecting artifacts in electroencephalograms (EEGs). The EEGs can be heavily corrupted by cardiac, eye movement, muscle and noise artifacts, so that EEG experts need to automatically detect them with a given level of confidence. In theory, the BMA methodology allows experts to evaluate the confidence in decision making most accurately. However, the non- stationary nature of EEGs makes the use of this methodology difficult. In our experiments with the sleep EEGs, the proposed BMA technique is shown to provide a better performance in terms of predictive accuracy.
Visual receptive fields of striate cortex neurons in awake monkeys.

THE EXPERIMENTS of Lettvin and colleagues (25) and Hubel and Wiesel (19) have indicated that a neuron in the vertebrate visual system is sensitive to particular characteristics of the visual stimulus. These characteristics have been referred to as the “trigger features” of the stimulus (3) and include such factors as size, shape, orientation, color, and rate and direction of movement; the area of the visual field over which the stimulus is effective is referred to as the receptive field of the neuron (17). Receptive-field characteristics of visual system neurons have subsequently been determined in a wide range of animals (for references see 7, 16). In addition, Hubel and Wiesel (19-24, 30) have shown that there are changes in the characteristics of the stimulus needed to activate a neuron at successively higher levels of the visual system. In both cats and monkeys they observed a progressive change in the predominant type of receptive field from the circular receptive fields of retinal ganglion and lateral geniculate cells, through the elongated receptive fields of the simplest cortical cells, to the more complex fields of other cortical neurons. This understanding of the visual system has been derived entirely from experiments in which the animal was paralyzed and frequently also anesthetized. Such procedures have been designed to eliminate all eye movements, particularly the small eye movements of physiological nystagmus that are characteristic of normal vision (1). The purpose of the present experiments was to determine whether receptive fields of striate cortex neurons in the awake, behaving animal are similar to those in the anesthetized,