Miracle-dz/UniFAR-FTUs huggingface dataset:
Extroversion is not a benefit in a task-based language classroom

There is common belief that the way we learn is influenced by our personality. Also, there is a contradiction between the predictions of psychologists and applied linguists in respect to the relationship between extraversion and learning as there is still a debate to know which personality type is in advantage. On one hand, psychologists maintain that extraversion is a disadvantage for learning since an extravert has less cortical arousal, has a limited long term memory, and is more easily inhibited. On the other hand, many applied linguists declare that extraversion is an advantage for learning L2 because the extraverts elicit more input and generate more outputs. To examine such controversies, a case study was designed to observe 7 extroverts and 7 introverts performing over various task based activities in an EFL language class in Iran. Primarily, an Eysenck's Personality Questionnaire-Revised in Persian Language was given to 140 students at pre-intermediate levels of an English Institute in Mashhad Iran to determine the dominant personality type among the students. Then, 14 students were randomly chosen so that their performances of task-based activities could be observed. The authors used a reliable checklist of Assessing Learners' Oral Proficiency (CALOP) for observing the students' performances of the three task types namely information-gap, reasoning-gap, and opinion-gap. The results revealed that extroversion was dominant (53. 6 %) in the population. More importantly, the introverts and extroverts showed no significant differences over their performances of the three task types.
Microstrip Tri-Mode Bandpass Filters Using Modified Ring Resonators

Two new microstrip tri-mode modified ring resonators (Resonator A and Resonator B) are presented and discussed through mode analysis method. The characteristic difference between these two resonators is that the center frequency of the tri-mode Resonator B is still the same as that of traditional dual-mode ring resonator, while the center frequency of Resonator A will be shifted up compared with that of traditional ring resonator. Based on these two novel resonators, two bandpass filter examples are designed, fabricated and measured. The simulations and measurements are in good agreement which validate the design ideas.
Phytochemical Evaluation and In vitro Antioxidant Activity of Various Solvent Extracts of Leucas aspera (Willd.) Link Leaves

Background: Free radicals initiate the oxidative stress and damage the healthy cells. These damages contribute ageing, cancer, cardiovascular and inflammatory diseases. Phenolic compounds, flavonoids and tannins are directly contributed to antioxidant activity. Objective: The present study was attempted to evaluate the phytochemicals present in various solvent extracts obtained from Leucas aspera (Willd) Link leaves ( L. aspera ) and its antioxidant activity using different in-vitro models. Materials and Meth-ods: The free radical scavenging and antioxidant activity of petroleum ether, ethanol, isopropyl alcohol, ethyl acetate and chloroform extracts of L. aspera leaves were assessed by different in-vitro models include DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging capacity, total antioxidant activity, ABTS (2,2,-azinobis (3-ethylbenzoline-6-sulfonic acid) radical scavenging activity and nitric oxide radical inhibition assay, in order to ensure the pharmacological effects of the plant. Results: Petroleum ether extract (non-polar solvent) showed better antioxidant activity among the solvents with IC 50 values 18.96 μg/mL (DPPH assay), 17.22 μg/mL (total antioxidant assay), 16.00 μg/mL (ABTS assay) and 11.87 μg/mL (nitric oxide scavenging). Whereas, ethanol extract (polar solvent) exhibited better DPPH scavenging, ABTS assay and nitric oxide scavenging activity (IC 50 =19.90, 11.60 and 13.47 μg/mL respectively) compared to chloroform, ethyl-acetate and isopropyl alcohol extracts. Conclusion: The results of our current study
Contractionanalysis of switched systemsvia regularization ⋆

We study incremental stability and convergence of switched (bimodal) Filippov systems via contraction analysis. In particular, by using results on regularization of switched dynamical systems, we derive sufficient conditions for convergence of any two trajectories of the Filippov system between each other within some region of interest. We then apply these conditions to the study of different classes of Filippov systems including piecewise smooth (PWS) systems, piecewise affine (PWA) systems and relay feedback systems. We show that contrary to previous approaches, our conditions allow the system to be studied in metrics other than the Euclidean norm. The theoretical results are illustrated by numerical simulations on a set of representative examples that confirm their effectiveness and ease of application.
A molecular toolbox for genetic manipulation of zebrafish

Zebrafish is an emerging model system in biomedical research. The high degree of evolutionary conservation in genetics as well as in development allows deciphering the molecular mechanisms underlying vertebrate development and human diseases. Zebrafish offers important advantages as a genetic model organism such as a completely sequenced genome, fast external embryonic development accompanied by nearly perfect transparency, a large number of offspring year round, and molecular accessibility for genetic manipulation. This review discusses established and newly developed approaches for genetic manipulation of zebrafish and their usefulness to understand gene function in various human disorders like heart defects or Alzheimer's disease.
(Dis-)integrative Effects of MUD-Usage as Seen by the Players

Most studies on social relationships in Multi-User-Dungeons (MUDs) focus on the online development of the relationships or on their trans¬fer from Virtual Reality (VR) to Real Life (RL). The perspective of my study is broader: 50 players of the German MUD Avalon were questioned in an online survey about the relationships they had gained or lost by playing online – and for their own judgement on the effects the MUD had on their social network. Of these 50, 20 participated in qualitative online interviews; their answers allow a better under¬standing of the quantitative data. The main results of the quantitative survey are that most of the players gained new relationships – in¬cluding an amazingly high number of couples – and only one player lost a friend; nevertheless nearly half of the players had neglected their RL relationships for some time. The interviewees judged these effects according to their RL experiences: Highest ratings were re-ported by those who gained closer relationships in VR than they were used to in RL – even though this might mean that they had simply found acquaintances rather than real friends.
Advances in Genetic Engineering of Primary Grain Legume Crops: a Review Study

In general, the seeds of many legumes are rich in both oil and protein; therefore, these grain legumes are commonly used as an important staple food worldwide. Transgenetic manipulation or production of transgenic crops has been needed for genetic enhancement as part of plant breeding programmes. This study is an attempt to evaluate the advancements in technology based on genetic transformation in major primary grain legume crops such as; chickpea, pigeon pea, garden pea, mung bean and lentil. Moreover, this study also involves to analyzing the screening of the competent explants as target tissues for gene delivery, mode of gene transformation, and further selection of transgenic tissues followed by regeneration of transgenic plantlets. Although most reports on introducing genes into grain legumes are based upon the use of Agrobacterium tumefaciens during transformation experiments, however, there have been also parallel efforts involving the use of biolistics and electroporation methods to produce transgenic grain legume crops with essential improved traits.
Speech Recognition of Czech—Inclusion of Rare Words Helps

Large vocabulary continuous speech recognition of inflective languages, such as Czech, Russian or Serbo-Croatian, is heavily deteriorated by excessive out of vocabulary rate. In this paper, we tackle the problem of vocabulary selection, language modeling and pruning for inflective languages. We show that by explicit reduction of out of vocabulary rate we can achieve significant improvements in recognition accuracy while almost preserving the model size. Reported results are on Czech speech corpora.
A Vision-based Transfer Learning Approach for Recognizing Behavioral Symptoms in People with Dementia

With an aging population that continues to grow, dementia is a major global health concern. It is a syndrome in which there is a deterioration in memory, thinking, be-havior and the ability to perform activities of daily living. Depression and aggressive behavior are the most upsetting and challenging symptoms of dementia. Automatic recognition of these behaviors would not only be useful to alert family members and caregivers, but also helpful in planning and managing daily activities of people with dementia (PwD). In this work, we propose a vision-based approach that unifies transfer learning and deep convolutional neural network (CNN) for the effective recognition of behavioral symptoms. We also compare the performance of state-of-the-art CNN features with the hand-crafted HOG-feature, as well as their combination using a basic linear SVM. The proposed method is evaluated on a newly created dataset, which is based on the dementia storyline in ITVs Emmerdale episodes. The Alzheimer’s Society has described it as a "realistic portrayal"1 of the condition to raise awareness of the issues surrounding dementia.
Seabird bycatch by tuna longline fisheries off southern Africa, 1998–2000

The incidental mortality of seabirds in tuna longline fisheries is estimated for the continental South African Exclusive Economic Zone (EEZ). Fishery observers accompanied 13 fishing trips and observed 108 sets (143 260 hooks) during the period 1998–2000. Despite most lines being set at night, seabird bycatch rates were high, with a mean of 1.6 birds killed per 1 000 hooks. Japanese vessels (1% effort observed) had a higher bycatch rate (2.6 birds per 1 000 hooks, range per trip 0.1–5.4) than South African vessels (0.8, range 0.0–4.3; 17% effort observed), possibly as a result of gear differences. Bird bycatch differed regionally in relation to the numbers of birds attending vessels. In international waters off the Northern Cape and southern Namibia, where there are few birds, only one bird was caught on 93 600 hooks (0.01 birds per 1 000 hooks). Shy Thalassarche cauta, black-browed T. melanophris and yellow-nosed T. chlororhynchos albatrosses, and white-chinned petrels Procellaria aequinoctialis were killed most frequently. Based on 1998–1999 fishing effort, simple extrapolation suggests that 19 000–30 000 seabirds are killed annually in South Africa's EEZ, of which 70% are albatrosses. Confidence in these estimates is low, given the small proportion of effort observed, but it is clear that urgent steps are needed to reduce seabird bycatch within South African waters.
Research on RSS Data Optimization and DFL Localization for Non-Empty Environments

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments.
On the Stability Analysis of Delay Differential-Algebraic Equations

The stability analysis of linear time invariant delay differentialalgebraic equations (DDAEs) is analyzed. Examples are delivered to demonstrate that the eigenvalue-based approach to analyze the exponential stability of dynamical systems is not valid for an arbitrarily high index system, and hence, a new concept of weak exponential stability (w.e.s) is proposed. Then, we characterize the w.e. s in term of a spectral condition for some special classes of DDAEs.
ESTIMATING THE COST OF EQUITY IN THE BALTIC REGION

During the last two decades the Baltic region has been subject to several episodes of investment volatility and political turmoil. Although financial liberalization processes undertaken in these countries could reduce the cost of equity, it seems that investors have been cautious in investing in the Baltic region. In this research, we estimate the cost of equity per industry sector in three Baltic countries (Estonia, Lithuania and Latvia) during the period 2005–2008 and conclude that the cost of equity seems to have increased during the period 2005–2007, indicating that the region was less financially integrated with the world capital market. </
Tax reform in the oil sector of Russia - a positive assessment

I analyze the system of taxes imposed on the Russian oil sector before and after the tax reform of 2002. I first establish a price parity relation explaining the gap between the world and domestic prices through export duties and alternative transportation costs. Based on it, I argue that the indirect taxes (VAT, the sales and excise taxes) do not decrease the net profits of oil producers, but are mostly shifted onto consumers. The tax burden on producers, on the other hand, consists of the export duties, resource payments, the profit tax and the property tax. I show that the tax reform had little effect on the total amount of taxes imposed on producers, but changed its structure across the types of taxes. After the reform the share of efficiently collected taxes, most importantly the extraction tax (ET), increased, explaining higher tax revenues. Finally, I estimate the extent to which the extraction tax is economically inefficient. By showing that the deadweight loss associated with ET is only a small fraction of the generated tax revenues, I argue that ET is a better choice for capturing the natural rent in the Russian oil sector than the poorly controlled profit tax. On the whole, this evidence depicts the reform of oil taxation in 2002 as a big step forward of the Russian fiscal system. This report is a shorter English version of the paper "Estimating the tax burden in the Russian oil sector under the price parity hypothesis" (Ekonomika i Matematicheskie Metody (2005), 41, 3).
Ranking and Selection Procedure for Gini Index

Gini index, which is derived from the Lorenz curve of income inequality and shows income inequality in different populations, can be applied to ranking and selectionpopulations. Many procedures are available for ordering and ranking income distributions where the ordering is not linear. However, the researchers often are not interested in ordering the populations but selecting the best (or worst) of available populations indicating a lower (or higher) level of disparities in incomes within the population.Madhuri S. Mulekar. (2005) discussed the estimation of overlap ofincome distributions and selection in terms of Gini Measure of income inequality.In this paper, we simulate populations ranking and selection based on Gini index of income inequality for case that the variances are equal but known in income distributions and for case that the variances are unequal but known in income distributions.
Long term variations of uranium isotopes and radiocarbon in the surface seawater recorded in corals

U-series isotopes and radiocarbon are extensively used for dating and as tracers of various processes during the late Quaternary. Advanced, mass spectrometric U-Th and radiocarbon isotope ratio measurement techniques, as opposed to detection of decay products, routinely achieve high temporal precision with smaller sample sizes. Thermal ionization mass spectrometry (TIMS) in U-series dating, and accelerator mass spectrometry (AMS) in 14C dating, have enabled significant advances in each field. In this paper, we review the techniques of TIMS U-series dating and AMS methods for radiocarbon dating as applied to corals. Coupled measurements of U-isotopes and 14C in corals provide enhanced insights into past changes in climate. Radiocarbon measurements in Papua New Guinea corals from Huon Peninsula terraces of the last glacial period, dated by U-series, show large discrete 14C peaks which appear to be correlated with reorganizations of the North Atlantic thermohaline circulation and Heinrich events. Residence time of Uranium in the oceans with respect to riverine inflow is estimated to be about 300–600 thousand years, a time span accessible to U-series disequilibrium dating of corals which concentrate U from seawater in their skeletons. Measurements of the 234U/238U ratio in the glacial-age Huon Peninsula corals, when combined with previous data, indicate long term variations in 234U in seawater by up to about 7%. The variations appear to be linked to major glacial-interglacial climate transitions and to sea-level changes.
Dextrose modified bilosomes for peroral delivery: improved therapeutic potential and stability of silymarin in diethylnitrosamine-induced hepatic carcinoma in rats

Abstract Hepatic carcinoma (HC) is one of the most prevalent cancers, ranked as the second most common cause of cancer-related deaths worldwide. Silymarin (SYL) has been reported for its anticarcinogenic activity against various types of cancer such as prostate, breast, ovary, colon, lung, bladder and liver. Due to poor solubility and low bioavailability SYL lacks satisfactory therapeutic value thus designing a suitable and effective delivery system of SYL can led to improved therapeutic potential. The present study was aimed to develop SYL-loaded dextrose (DEX) modified bilosomes for targeted delivery to HC cells. The DEX-modified bilosomes were prepared through thin-film hydration method and optimized employing Box Behnken design. The bilosomes were evaluated for percent entrapment, drug loading, in vitro release and cytotoxicity on Hep-G2 cells. The optimized DEX-SYL-BL exhibited a particle size of 219.3 ± 2.99 nm, percent entrapment of 62.32 ± 4.23%, drug loading of 34.56 ± 1.23% and 84.96 ± 2.76% drug release respectively over a period of 24 hr. The stability of bilosomes was ascertained in simulated gastric and intestinal fluids. Cytotoxicity studies revealed greater performance of DEX-SYL-BL in terms of reduced viability in Hep-G2 cell lines when compared with pure SYL and SYL-BL. Further DEX-modified bilosomes were evaluated in vivo for their therapeutic efficacy in DEN-induced (Diethylnitrosamine) hepatic carcinoma in animal model. The DEX-SYL-BL displayed higher therapeutic potential as revealed from enhanced survival and reduced tumour burden in animals. DEX-SYL-BL also displayed significant restoration of altered oxidative markers and SGOT, SGPT levels towards normal value when compared with pure SYL.
Computational design of metal-supported molecular switches: transient ion formation during light- and electron-induced isomerisation of azobenzene

In molecular nanotechnology, a single molecule is envisioned to act as the basic building block of electronic devices. Such devices may be of special interest for organic photovoltaics, data storage, and smart materials. However, more often than not the molecular function is quenched upon contact with a conducting support. Trial-and-error-based decoupling strategies via molecular functionalisation and change of substrate have in many instances proven to yield unpredictable results. The adsorbate-substrate interactions that govern the function can be understood with the help of first-principles simulation. Employing dispersion-corrected density-functional theory (DFT) and linear expansion delta-self-consistent-field DFT, the electronic structure of a prototypical surface-adsorbed functional molecule, namely azobenzene adsorbed to (1 1 1) single crystal facets of copper, silver and gold, is investigated and the main reasons for the loss or survival of the switching function upon adsorption are identified. The light-induced switching ability of a functionalised derivative of azobenzene on Au(1 1 1) and azobenzene on Ag(1 1 1) and Au(1 1 1) is assessed based on the excited-state potential energy landscapes of their transient molecular ions, which are believed to be the main intermediates of the experimentally observed isomerisation reaction. We provide a rationalisation of the experimentally observed function or lack thereof that connects to the underlying chemistry of the metal-surface interaction and provides insights into general design strategies for complex light-driven reactions at metal surfaces.
American Sign Language fingerspelling recognition from video : Methods for unrestricted recognition and signer-independence by Taehwan

In this thesis, we study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL, and recognizing it is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work, we propose several types of recognition approaches, and explore the signer variation problem. Our best-performing models are segmental (semi-Markov) conditional random fields using deep neural network-based features. In the signer-dependent setting, our recognizers achieve up to about 8% letter error rates. The signer-independent setting is much more challenging, but with neural network adaptation we achieve up to 17% letter error rates. Thesis Supervisor: Karen Livescu Title: Assistant Professor