Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of gill cytosolic corticosteroid receptors in juvenile Atlantic salmon: interaction effects of growth hormone with prolactin and triiodothyronine.

The potential effects of growth hormone (GH), prolactin (Prl), and triiodothyronine (T3) on gill Na+,K+-ATPase activity and corticosteroid receptor (CR) concentration (Bmax) and dissociation constant (Kd) were examined in juvenile Atlantic salmon (Salmo salar). Compared to controls, fish injected with GH (ovine, 5.0 microgram g-1) had significantly greater gill Na+,K+-ATPase activity after 7 and 14 days. Gill CR Bmax and Kd were significantly elevated on day 7, but not day 14. T3 also significantly increased CR Bmax. The effect of GH on CR Bmax was also additive with T3 (5.0 microgram g-1) treatment. There was a synergistic effect on CR Bmax when purified coho salmon GH (csGH, 0.1 microgram g-1) was injected in combination with T3 (1.6 microgram g-1). Prl (ovine, 5.0 microgram g-1; purified coho salmon, 0.1 microgram g-1) did not significantly alter gill CR Bmax. Although Prl limited the increase in CR Bmax by GH, the effect was not signicant. T3 and Prl did not have an effect on Kd. GH significantly increased gill Na+,K+-ATPase activity, T3 administration did not have a significant effect, and Prl-treated fish had significantly lower gill Na+,K+-ATPase activity. The results indicate that T3 acts additively with GH, while Prl has no effect in regulating CR Bmax. An increase in cytosolic CR by GH and T3, but not Prl, may regulate gill responsiveness to cortisol and be an important mechanism in the endocrine control of physiological changes during the parr-smolt transformation.
Effects of a Bacteria-Based Probiotic on Ruminal pH, Volatile Fatty Acids and Bacterial Flora of Holstein Calves

ABSTRACT Twelve ruminally cannulated Holstein calves (age, 12 ± 3 weeks) were used to identify the effect of a probiotic comprised of Lactobacillus plantarum, Enterococcus faecium and Clostridium butyricum on ruminal components. The calves were adapted to a diet containing a 50% high-concentrate (standard diet) for 1 week, and then, the probiotic was given once daily for 5 days (day 1–5) at 1.5 or 3.0 g/100 kg body weight to groups of four calves each. Four additional calves fed the standard diet without probiotic served as the corresponding control. Ruminal pH was measured continuously throughout the 15-day experimental period. Ruminal fluid was collected via a fistula at a defined time predose and on days 7 and 14 to assess volatile fatty acid (VFA), lactic acid and ammonia-nitrogen concentrations, as well as the bacterial community. The probiotic at either dose improved the reduced 24-hr mean ruminal pH in calves. The circadian patterns of the 1 hr mean ruminal pH were identical between the probiotic doses. In both probiotic groups, ruminal lactic acid concentrations remained significantly lower than that of the control. Probiotic did not affect ruminal VFA concentrations. L. plantarum and C. butyricum were not detected in the rumen of calves given the high-dose probiotic, whereas Enterococcus spp. remained unchanged. These results suggest that calves given a probiotic had stable ruminal pH levels (6.6–6.8), presumably due to the effects of the probiotic on stabilizing rumen-predominant bacteria, which consume greater lactate in the rumen.
On the Roles of Stereotype Activation and Application in Diminishing Implicit Bias

Stereotypes can influence social perception in undesirable ways. However, activated stereotypes are not always applied in judgments. The present research investigated how stereotype activation and application processes impact social judgments as a function of available resources for control over stereotypes. Specifically, we varied the time available to intervene in the stereotyping process and used multinomial modeling to independently estimate stereotype activation and application. As expected, social judgments were less stereotypic when participants had more time to intervene. In terms of mechanisms, stereotype application, and not stereotype activation, corresponded with reductions in stereotypic biases. With increasing time, stereotype application was reduced, reflecting the fact that controlling application is time-dependent. In contrast, stereotype activation increased with increasing time, apparently due to increased engagement with stereotypic material. Stereotype activation was highest when judgments were least stereotypical, and thus, reduced stereotyping may coincide with increased stereotype activation if stereotype application is simultaneously decreased.
A Study on Contour Line and Internal Area Extraction Method by using the Self-Organization Map

Recently, intelligent transport system (ITS) is actively developed. There are big development areas in it. Within those areas, the research on detection and recognition of the road sign is one of the most important development areas. In this paper, we show the process improvement in order to extract the contour line and recognize the road sign shape from it. We have proposed the new extraction method of the contour line and internal areas from the road sign instantaneously by using the self organization map (SOM)
Use and Analysis on Cyclomatic Complexity in Software Development

: Cyclomatic complexity is software metric used in software developments as White box testing and structural testing. The purpose of the paper is to describe the Use and Analysis on Cyclomatic complexity in Software development with an example. The Cyclomatic complexity is computed using the flow graph of the program: the nodes of the graph correspond to one or more code statement and the edges connect two nodes. Based on the flow graph how to find Cyclomatic complexity is described here.
Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine

Collaborative filtering has been widely used in modern recommender systems to provide accurate recommendations by leveraging historical interactions between users and items. The presence of cold-start items and users has imposed a huge challenge to recommender systems based on collaborative filtering, because of the unavailability of such interaction information. The factorization machine is a powerful tool designed to tackle the cold-start problems by learning a bilinear ranking model that utilizes content information about users and items, exploiting the interactions with such content information. While a factorization machine makes use of all possible interactions between all content features to make recommendations, many of the features and their interactions are not predictive of recommendations, and incorporating them in the model will deteriorate the generalization performance of the recommender systems. In this paper, we propose an efficient Sparse Factorization Machine (SFM), that simultaneously identifies relevant user and item content features, models interactions between these relevant features, and learns a bilinear model using only these synergistic interactions. We have carried out extensive empirical studies on both synthetic and real-world datasets, and compared our method to other state-of-the-art baselines, including Factorization Machine. Experimental results show that SFM can greatly outperform other baselines.
Masked mycotoxins: A review

The aim of this review is to give a comprehensive overview of the current knowledge on plant metabolites of mycotoxins, also called masked mycotoxins. Mycotoxins are secondary fungal metabolites, toxic to human and animals. Toxigenic fungi often grow on edible plants, thus contaminating food and feed. Plants, as living organisms, can alter the chemical structure of mycotoxins as part of their defence against xenobiotics. The extractable conjugated or non-extractable bound mycotoxins formed remain present in the plant tissue but are currently neither routinely screened for in food nor regulated by legislation, thus they may be considered masked. Fusarium mycotoxins (deoxynivalenol, zearalenone, fumonisins, nivalenol, fusarenon-X, T-2 toxin, HT-2 toxin, fusaric acid) are prone to metabolisation or binding by plants, but transformation of other mycotoxins by plants (ochratoxin A, patulin, destruxins) has also been described. Toxicological data are scarce, but several studies highlight the potential threat to consumer safety from these substances. In particular, the possible hydrolysis of masked mycotoxins back to their toxic parents during mammalian digestion raises concerns. Dedicated chapters of this article address plant metabolism as well as the occurrence of masked mycotoxins in food, analytical aspects for their determination, toxicology and their impact on stakeholders.
Öğretmenlik Mesleği İş Özellikleri Ölçeğinin (ÖMİÖÖ) Geliştirilmesi: Geçerlik ve Güvenirlik Çalışması

Bu calismanin amaci, ogretmenlik mesleginin is ozelliklerini, ogretmen goruslerine gore incelemeye elverisli bir olcek gelistirmektir. Olcegin gelistirilmesi surecinde Hackman ve Oldham’in is ozellikleri modeli esas alinarak ogretmenlik meslegine uygun bir madde havuzu olusturulmus, ardindan kapsam gecerligi icin uzman gorusune basvurulmustur. Kapsam gecerligi calismasi sonrasina son hali verilen taslak olcek, Ankara ilinde yer alan resmi ortaogretim kurumlarinda gorevli 413 ogretmene uygulanmistir. Katilimcilardan toplanan olcekler uzerinde acimlayici faktor analizi (AFA) ve dogrulayici faktor analizi (DFA) uygulanarak, olcegin faktor yapisi incelenmistir. Olcegin guvenirligi ise Cronbach alfa katsayisi ile degerlendirilmistir. AFA ve DFA sonrasinda Ogretmenlik Meslegi Is Ozellikleri Olceginin (OMIOO) 35 maddeli ve bes faktor yapisinda gecerli bir olcek oldugu belirlenmistir. Cronbach alfa katsayisi hesaplamalari ise OMIOO’nun guvenilir bir olcek oldugunu ortaya koymustur. Bu bulgulara dayali olarak OMIOO’nun gecerlik ve guvenilir bir veri toplama araci oldugu sonucuna ulasilmistir.
A knowledge-based approach to cardiac signal analysis using LabVIEW

Data acquisition and analysis of various cardiac /transducer signals based on virtual instrument technology is gaining importance. This paper introduces a novel way of automating the diagnosis of cardiac disorders using an expert system developed on the basis of information derived from the analysis of Electrocardiogram (ECG) and also provides the online monitoring of cardiac patient. Cardiologists use ECG as a definitive indicator of the condition of the human heart using certain well-defined rules and their own experience to diagnose the condition of patient. The decision process is made systematic by developing a visualization tool using proven algorithm for processing the information contained in the typical ECG. The information can be of great help to a general practitioner, to identify the specific problem and start treatment without any delay. The proposed system provides a fast and effective diagnosis of the problem. This has been possible by encoding the expert knowledge and interfacing it with real time ECG available. The system has fast updating facility to track any rapid deterioration, if it occurs and is thus helpful to monitor patients in ICU.
Cross-Domain Scientific Collaborations Prediction with Citation Information

Cross-domain Scientific Collaborations have promoted rapid development of science and generated many innovative breakthroughs. However, predicting cross-domain scientific collaboration problem is rarely studied and collaboration recommendation methods within single domain cannot be directly utilized for solving cross-domain problems. In this paper, we propose a Hybrid Graph Model, which combines both explicit co-author relationships and implicit co-citation relationships together to construct a hybrid graph and then Random Walks with Restarts concept is used to measure and rank relatedness. The experiments with large publication data set show that Hybrid Graph Model outperforms some baseline approaches on several recommendation metrics. Citation information has been demonstrated to be very helpful for scientific collaboration recommendations as well.
Fast Recursive Portfolio Optimization

Institutional equity portfolios are typically constructed via taking expected stock returns and then applying the computationally expensive processes of covariance matrix estimation and mean-variance optimization. Unfortunately, these computational costs make it prohibitive to comprehensively backtest and tune higher frequency strategies over long histories. In this paper, we introduce a recursive algorithm which significantly lowers the computational cost of calculating the covariance matrix and its inverse as well as an iterative heuristic which provides a very fast approximation to mean-variance optimization. Together, these techniques cut backtesting time to a fraction of that of standard techniques. Where possible, the additional step of caching pre-calculated covariance matrices, can result in overall backtesting speeds up to orders of magnitude faster than the standard methods. We demonstrate the efficacy of our approach by selecting a prediction strategy in a fraction of the time taken by standard methods.
Maximum constant boost control of the Z-source inverter

This paper proposes two maximum constant boost control methods for the Z-source inverter, which can obtain maximum voltage gain at any given modulation index without producing any low-frequency ripple that is related to the output frequency. Thus the Z-network requirement will be independent of the output frequency and determined only by the switching frequency. The relationship of voltage gain to modulation index is analyzed in detail and verified by simulation and experiment.
Effect of Roadside APs in VANETs: A Comparison Study

In recent years, Vehicular Ad-hoc Networks (VANETs) have emerged as a unique type of MANETs. VANETs are an important component of the Intelligent Transportation Systems (ITS). They can been utilized to guarantee road safety, avoid potential accidents and create new forms of inter-vehicle communications. In this paper, we investigate the performance of OLSR protocol for video streaming application. The simulations are conducted in urban environment in two scenarios, where roadside Access Points (AP) are present and not present. We consider 802.11p standard and send multiple video streaming flows over UDP. We use throughput, delay and jitter as evaluation metrics. Based on simulation results, we noticed that transmission for streaming is improved when roadside AP are used.
Question Classification using HDAG Kernel

This paper proposes a machine learning based question classification method using a kernel function, Hierarchical Directed Acyclic Graph (HDAG) Kernel. The HDAG Kernel directly accepts structured natural language data, such as several levels of chunks and their relations, and computes the value of the kernel function at a practical cost and time while reflecting all of these structures. We examine the proposed method in a question classification experiment using 5011 Japanese questions that are labeled by 150 question types. The results demonstrate that our proposed method improves the performance of question classification over that by conventional methods such as bag-of-words and their combinations.
Discriminating Neutral and Emotional Speech using Neural Networks

In this paper, we address the issue of speaker-speciﬁc emotion detection (neutral vs emotion) from speech signals with models for neutral speech as reference. As emotional speech is produced by the human speech production mechanism, the emotion information is expected to lie in the features of both excitation source and the vocal tract system. Linear Prediction residual is used as the excitation source component and Linear Prediction Coef-ﬁcients as the vocal tract system component. A pitch synchronous analysis is performed. Separate Autoassociative Neural Network models are developed to capture the information speciﬁc to neutral speech, from the excitation and the vocal tract system components. Experimental results show that the excitation source carries more information than the vocal tract system. The accuracy neutral vs emotion classiﬁcation using excitation source information is 91% , which is 8% higher than the accuracy obtained us-ing vocal tract system information. The Berlin EMO-DB database is used in this study. It is observed that, the proposed emotion detection system provides an improvement of approximately 10% using excitation source features and 3% using vocal tract system features over the recently proposed emotion detection which uses the energy and pitch contour modeling with functional data analysis.
An Efficient Edge-Based Authentication for Network Coding against Entropy Attacks

This paper proposes a new edge-based authentication scheme for network coding. Many authentication schemes for random linear network coding have been proposed against pollution attacks. However, random linear network coding is vulnerable to entropy attacks. An adversary can generate messages that are verified as correct messages by the authentication mechanism but obstruct the network coding. Random linear network coding is shown to be efficient in a random failure model, but not in an adversary model. This paper shows a simple solution to tolerate entropy attacks by changing random linear coding to deterministic message combining rule. For an example, this paper shows a modification of RIPPLE, an authentication scheme for random linear network coding. Lastly, we show that the total delay of modified RIPPLE can be reduced by an edge-based authentication. RIPPLE and many other authentication schemes are node-based, that is, verification keys and operations are defined for each node. We show that we can construct an edge-based scheme, that is, verification keys and operations are defined for each edge. We show that the edge-based authentication scheme is more efficient than the node-based schemes.
Interactive in‐vehicle guidance through a multihierarchical representation of urban maps

Small computers used for assisting drivers (mostly in finding routes) have been growing in popularity in the past years. These systems are inherently interactive, but up to now this interaction is tackled under rather simple approaches. For example, current routing computer assistants consider only the shortest or the quickest route to a destination, although in certain situations it could be interesting for the driver to take into consideration other factors, such as the criminal rate, land value, or the beauty of the areas to be traversed. On the other hand, the interactive processes between the driver and the routing assistant are still very limited: They only enable the user to discard (or suggest) particular locations through a fixed set of names, i.e. street's names. This paper proposes a novel interactive mechanism for in‐vehicle routing that uses topological information at different levels of detail and a multihierarchical representation of urban maps. These hierarchical representations permit the system not only to plan routes efficiently but also to report them at different levels in detail in a human‐like set of symbols adapted to each user. This enhances the human–computer interaction during the routing process, increasing driver satisfaction. We illustrate our technique through a case of study in the city of Málaga (Spain). © 2010 Wiley Periodicals, Inc.
Teaching Unit Testing using Test-Driven Development

Of all testing levels, the unit level of testing has undergone the most recent and most dramatic change. With the introduction of new agile (aka, “lightweight”) development methods, such as XP (eXtreme Programming) came the idea of Test-Driven Development (TDD). TDD is a software development technique that melds program design, implementation and testing in a series micro-iterations that focus on simplicity and feedback. Programmer tests are created using a unit testing framework and are 100% automated.
Converse mode piezoelectric coefficient for lead zirconate titanate thin film with interdigitated electrode

The use of interdigitated electrodes (IDEs) in conjunction with ferroelectric thin films shows many attractive features for piezoelectric MEMS applications. In this work, growth of {1 0 0}-textured lead zirconate titanate (PZT) thin films was achieved on insulating MgO buffered, oxidized silicon substrates. IDEs were fabricated by lift-off techniques and cantilevers were formed by dicing. The deflection upon application of a sweeping voltage was measured as large signal response in parallel to the ferroelectric polarization (PV loop). Likewise, the small signal piezoelectric response was measured in parallel to the capacitance-voltage (CV) measurement. In this way, a complete picture of the ferroelectric–piezoelectric element was obtained. From the deflection, the in-plane piezoelectric stress in the PZT thin film was derived and, from this, the effective piezoelectric coefficients. For the latter, two types were defined: an engineering type corresponding to the average value along the IDE, which can directly be compared to coefficient of a parallel plate electrode (PPE) capacitor and a second one that approximately yields the idealized coefficient governing between the electrode fingers. The IDE structures were experimentally compared with PPE structures of identical film thickness. The resulting coefficients were of opposite sign, as expected. In spite of a much better polarization loop, the IDE device showed a lower average piezoelectric stress. The estimated peak value between the fingers was about the same as in the PPE device, corresponding to about 20 C m−2. Nevertheless, the result is very promising for cases where compressive piezoelectric stresses are required and for preventing cracking due to large piezoelectric tensile stresses in PPE systems.
The cross-course software engineering project at the NTNU: four years of experience

Many software engineering courses include all-term projects to convey principles relating to large-scale multi-person development. But even such projects will easily be too small and simple, unless a sufficient amount of study time is allocated to them. This time may be hard to find, especially in strictly programmed profession studies where a lot of general theory courses have to be taken. This paper reports on the experiences from a software engineering project where the solution to the above problem has been to have several courses share one project. This had some advantages. First of all, it allows time for a bigger and more complex project with reasonable sacrifices of "own time" in each of the participating courses. Equally important, it is possible to show connections between the courses. In spite of these advantages, there have also been problems with the project, still leaving room for improvement.
Multiresolution modeling and visualization of volume data based on simplicial complexes

A scattered volumetric dataset is regarded as a sampled version of a scalar field defined over a three-dimensional domain, whose graph is a hypersurface embedded in a fourdimensional space. We propose a multiresolution model for the representation and visualization of such dataset, based on a decomposition of the three-dimensional domain into tetrahedra. Multiresolution is achieved through a sequence of tetrahedralizations that approximate the scalar field at increasing precision. The construction of the model is based on an adaptive incremental approach driven by the local coherence of the scalar field. The proposed model allows an efficient extraction of compact isosurfaces with adaptive resolution levels as well as the development of progressive and multiresolution rendering approaches. Experimental evaluations of the proposed approach on different scattered datasets are reported.