Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the Level of Transportation Costs in the Core-Periphery Model: a Majority Voting Approach

We analyse the political determination of transportation costs in an analytically solvable core-periphery model. In a benchmark case with certainty about where agglomeration takes place, we find that a majority of voters prefers low trade costs and the resulting equilibrium is an industrialised core and a de-industrialised periphery. Allowing for uncertainty we show that a high trade cost candidate, that guarantees the initial symmetric equilibrium, may defeat the core-periphery equilibrium candidate. The reason is that a coalition of risk-averse immobile factors of production votes for status quo due to uncertainty about which region that will attract industrial activity.
Lepton mass effects and angular observables in Λ b →Λ (→p π) ℓ + ℓ -

The flavor changing rare decay $B\to K^{*}(\to K\pi)\ell^+\ell^-$ is one of the most studied modes due to its sensitivity to physics beyond the standard model and several discrepancies have come to light among the plethora of observables that are measured. In this paper we revisit the analogous baryonic decay mode $\Lambda_{b}\rightarrow \Lambda (\to p\pi) \ell^{+}\ell^{-}$ and we present a complete set of ten angular observables that can be measured using this decay mode. Our calculations are done retaining the finite lepton mass so that the signal of lepton non-universality observed in $B\to K^{*} \ell^+\ell^-$ can be corroborated by the corresponding baryonic decay mode. We show that due to the parity violating nature of the subsequent $\Lambda\to p\pi$ decay there exist at least one angular asymmetry that is non-vanishing in the large recoil limit unlike the case in $B\to K^{*}\ell^+\ell^-$ decay mode, making it particularly sensitive to new physics that violates lepton flavor universality.
The Environmental Values of Potential Ecotourists: A Segmentation Study

Although sustainable tourism that contributes to biodiversity protection seems to be important anywhere in the planet, ecotourism is rarely examined as a rural development opportunity outside the context of ‘mega-diverse’ countries, i.e. those 12 countries1 that between them harbour 60–70% of the total biodiversity of the planet. Collecting data from 20 sites around Scotland, this study considered the potential of ecotourism development in this country through a market segmentation study based on the environmental values of potential ecotourists. With the assistance of the New Ecological Paradigm (Dunlap et al., 2000) four segments with a range of anthropocentric and ecocentric values were identified. Results indicate that demand for ecotourism is not confined to ecocentric segments and that biodiversity protection is prioritised by all segments as the most salient ecotourism attribute, although each segment attaches a different intensity to its importance. The study identifies visitor interest for a Scottish ecotourism experience that emphasises biodiversity conservation and low use of exhaustible resources, while providing facilities for wildlife watching, hill walking and relaxing. Although segment attitudes towards ecotourism do not differ significantly, environmental values can be used to segment potential ecotourists as they allow the formation of visitor groups with different trip characteristics.
FANS WHO BET AGAINST THEIR FAVORITE TEAMS 2 An Investigation of Highly Identified Fans Who Bet Against their Favorite Teams Highlights

Using a mixed-method exploratory approach we describe and explain the seemingly nonnormative behaviors of highly identified fans who Bet Against their Favorite Teams (BAFT). Axial coding of qualitative data from 190 survey respondents and two focus groups indicates the emergence of common themes allowing a typology to unfold that explains the motives for and against BAFTing. Results reveal that Gamblers BAFT for reasons un-related to fandom. Hedgers, on the other hand, BAFT precisely because they are fans; they offset a perceived impending emotional loss with a financial gain, a behavior we identify as Hedging Against Future Failure (HAFFing). This research expands the theoretical knowledge of indirect tactics of image management and introduces HAFFing as a transactional, proactive, and private coping mechanism utilized by highly identified fans to regulate their psychological health. Beyond the implications for researchers of self-image management, these results are applicable to global sport managers adapting to the rising prominence and societal acceptance of sport gambling.
The consequences of literacy and schooling for parsing strings

ABSTRACT Word processing initially occurs through letter-by-letter parsing at early stages of reading development. Here we investigated the role of literacy in parsing both linguistic and non-linguistic strings, as well as the consequences of the possible changes brought by the acquisition of reading. Illiterates matched with schooled literates on socio-demographic and cognitive measures were presented with a character search task. Overall, literates performed better than illiterates in identifying constituents in linguistic and non-linguistic strings. Illiterates showed a similar performance for all types of strings. In contrast, literates showed a graded pattern with non-linguistic strings being processed much worse than linguistic strings. These results support domain-specific models of orthographic processing, and they suggest that visual word recognition is not fully parallel to visual object recognition. Most importantly, they demonstrate the impact of literacy on the ability of breaking down a word into its constituents.
PUNCTUACTION RECONSTRUCTION FOR AUTO- MATIC SPEECH TRANSCRIPTION

This thesis deals with the problem of punctuation reconstruction in the output of automatic speech recognition systems. Constrains given on the solutions were applicability on general spoken English language and reasonable accuracy of the punctuation prediction system. Natural language tends to have in some cases non-deterministic nature and usually consists of a large number of grammatic rules. Therefore, a machine learning approach was chosen to solve this problem for its ability to recognize complicated patterns in data. A number of experiments with recurrent neural networks were executed to find the best network architecture for punctuation prediction. Resulting models created during these experiments reach accuracy comparable if not better than the works currently held as state-of-the-art solutions for punctuation reconstruction. Abstrakt Táto práca sa zaoberá rekonštrukciou interpunkcie vo výstupoch systémov na automatický prepis reči. Výsledný systém by mal byť schopný rekonštruovať interpunkciu vo všeobecnej zväčša hovorenej angličtine s rozumnou mierou presnosti. Prirodzený ľudský jazyk sa v istých prípadoch sa môže javiť nedeterministický a tvorba reťazcov často podlieha veľkému množstvu gramatických pravidiel. Kvôli tomu boli na predikciu interpunkcie vybrané algoritmy strojového učenia pre ich schopnosť rozoznať komplikované vzory v dátach. Bolo vykonaných niekoľko experimentov s rekurentnými neurónovými sieťami za účelom nájdenia najvhodnejšej architektúry modelu. Výsledné modely vytvorené počas týchto experimentov dosahujú presnosť porovnateľnú ak nie lepšiu než práce, v súčasnosti považované za najlepšie v obore.
Isolation of Quercetin and Kaempferol From Azadirachta Indica Leaves and Its Synergistic Action Against Clinically Isolated Multi Drug Resistant Strains MRSA

The aim of this study was to isolate the two major flavonoids Quercetin and kaempferol by P-HPLC from Azadirachta Indica leaves and its activity against clinical isolated MRSA strains. The flavonoids were separated and identified by analytical column, while isolated by preparative column Intersil ODS-3 C-18 and its composition as in analytical procedure. Antibiotics (Cefixime, Ceftriaxone, Erythromycin, Ciprofloxacin, Cephradine, Methicillin, Ampicillin, Levoflaxacin and Amoxicillin) and flavonoids (quercetin, kaempferol and combination of quercetin + kaempferol) were evaluated against clinically isolated MRSA. The results showed that the Cefixime, Ceftriaxone, Ciprofloxacin, Cephradine, Methicillin, Ampicillin and Amoxicillin were found inactive against clinical isolated MRSA when tested alone, but when combine with quercetin, kaempferol and Q+K showed valuable anti-MRSA activity, while tested flavonoids showed were found to be no impact on Erythromycin and causing reduction in Levoflaxacin and Ciprofloxacint.
An analysis of credit scoring for agricultural loans in Thailand

Loan contract performance determines the profitability and stability of the financial institutions and screening the loan applications is a key process in minimizing credit risk. Before making any credit decisions, credit analysis (the assessment of the financial history and financial backgrounds of the borrowers) should be completed as part of the screening process. A good credit risk assessment assists financial institutions on loan pricing, determining amount of credit, credit risk management, reduction of default risk and increase in debt repayment. The purpose of this study is to estimate a credit scoring model for the agricultural loans in Thailand. The logistic regression and Artificial Neural Networks (ANN) are used to construct the credit scoring models and to predict the borrower’s creditworthiness and default risk. The results of the logistic regression confirm the importance of total asset value, capital turnover ratio (efficiency) and the duration of a bank - borrower relationship as important factors in determining the creditworthiness of the borrowers. The results also show that a higher value of assets implies a higher credit worthiness and a higher probability of a good loan. However, the negative signs found on both capital turnover ratio and the duration of bank-borrower relationship, which contradict with the hypothesized signs, suggest that the borrower who has a long relationship with the bank and who has a higher gross income to total assets has a higher probability to default on debt repayment.
Growing Together: Assessing Equity and Efficiency in an Early-Life Health Program in Chile

We study the mechanism of action of an early-life social safety net program, and quantify its impact on child health outcomes at birth. We consider both the equity and efficiency implications of program impacts, and provide a metric to compare various such programs around the world. In particular, we estimate the impact of participation in Chile Crece Contigo (ChCC), Chile's flagship early-life health and social welfare program, using administrative birth data matched to social benefits usage. We find that this targeted social program had significant effects on birth weight (approximately 10 grams) and other early life human capital measures. These benefits are largest among the most socially vulnerable groups, however shift outcomes toward the middle of the distribution of health at birth. We show that the program is efficient when compared to other successful neonatal health programs around the world, and find some evidence to suggest that maternal nutrition components and increased links to the social safety net are important mechanisms of action.
Effectiveness of Learning to Rank for finding User Similarity in Social Media

paper focuses on an automatic and accurate approach for finding similar users in social networks. Many types of social networks could benefit from such techniques, but the focus in this paper is on online photo services. The similarity between users needs to be considered on two different levels, i.e., the semantic similarity (or correspondence in tagging behavior), and the similarity in terms of social relations. In recent work, heuristic formulas were introduced for the tag commonness (TC) and the link strength (LS), with an adaptive combination scheme to describe how relevant each of these similarity aspects are for particular users, in order to define the user similarity. This paper presents an experiment, where a Learning-to-Rank approach is used to find suitable combinations of TC and LS related parameter values, hence taking into account the proficiency of users to tag their photos, and their noticeability in the online community, in order to obtain an overall user similarity. The user experiments show that the results with this learning-to-rank approach are significantly better than with a former, heuristic, approach.
A software approach to distributing requests for DNS service using GNU Zebra, ISC BIND 9 FreeBSD

This paper describes an approach for deploying authoritative name servers using a cluster of hosts, across which the load of client requests is distributed. DNS services deployed in this fashion enjoy high availability and are also able to scale to increasing request loads in a straightforward manner. The approach described here does not employ any custom load-balancing appliances (e.g. devices commonly marketed as as "layer-four switches", "content switches" or "load-balancers"); instead the individual members of the cluster announce a service address to one or more gateway routers by participating in routing protocols to provide an intra-cluster anycast architecture. The F Root Name Server is deployed using clusters built in this fashion, using FreeBSD [1], GNU Zebra [2] and ISC BIND 9[3].
Correlation of parameter estimators for models admitting multiple parametrizations

When estimating parameters using noisy data, uncertainty quan- tification methods provide a way to investigate the confidence one has in the parameter estimates, as well as to obtain information on the possible depen- dence of parametric estimators on one another. In this note, we consider un- certainty quantification techniques that allow visualization of the distributions of these parameter estimators for evidence of possible correlation. We consider three mathematical models (the logistic curve, the Richards curve, and the spring equation), which permit multiple parametrizations, and compare the cor- responding parameter estimators for possible dependence/independence. The uncertainty quantification techniques we employ include the correlation coeffi- cients, asymptotic as well as exact confidence regions or ellipsoids, and Monte Carlo plots generated by the DRAM algorithm.
Pressure perturbations in and below trade wind cumulus clouds: Forcing patterns

Convective motions occur as a response to pressure fluctuations, and over the trade wind region, cumulus activity is ubiquitous to the top of the trade wind inversion, and sometimes considerably higher. Largescale forcings do exist, but the trade-wind cumuli organize into bands and clumps on a much finer scale that the large-scale waves would dictate. On the large scale, the trade winds blow at steady speeds towards the ITCZ, but on a smaller scale there are often considerable deviations from the large-scale mean wind speed. In the mixed layer, the deviations from the mean speed are often caused by precipitation-induced cold pools. As trade wind cumuli precipitate, part of the precipitation may evaporate below cloud base in the mixed layer. This can lead to local cooling of up to 2 C in the mixed layer, and the cooling should contribute to a high pressure being associated with the cold pools. Such cold pools will tend to spread out, thereby providing divergence immediately below the center of the cold pool. At the edges of the cold pool, there may be strong convergence which may lead to the triggering of new convection.
TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.
Development and Validation of Generalized Lifting Line Based Code for Wind Turbine Aerodynamics

In order to accurately model large, advanced and ef ficient wind turbines, more reliable and realistic aerodynamic simulation tools are nece ssary. Most of the available codes are based on the blade element momentum theory. These codes are fast but not well suited to properly describe the physics of wind turbines. On the other hand, by using computational fluid-dynamics codes, in which full Navier-Stokes e quations are implemented, a strong expertise and a lot of computer time to perform analyses are required. A code, based on a generalized form of Prandtl’s lifting line in combi nation with a free wake vortex wake has been developed at Energy research Centre of Netherlands. In the present work, the development of this new code is presented, together with the results coming from numericalexperimental comparisons. The final part of the work is dedicated to the analysis of innovative configurations like winglets and curved blades.
Evaluating a Structural Model Forecast: Decomposition Approach

Macroeconomic forecasters are often criticized for a lack of transparency when presenting their forecasts. To deter such criticism, the transparency of the forecasting process should be enhanced by tracing and explaining the effects of data revisions and expert judgment updates on variations in the forecasts. This paper presents a forecast decomposition analysis framework designed to examine the differences between two forecasts generated by a linear structural model. The differences between the forecasts considered can be decomposed into the contributions of various forecast elements, such as the effect of new data or expert judgment. The framework allows us to evaluate the contributions of forecast assumptions in the presence of expert judgment applied in the expected way. The simplest application of this framework examines alternative forecast scenarios with different forecast assumptions. Next, a one-period difference between the forecastsâ€™ initial periods is added to the examination. Finally, a replication of the Inflation Forecast Evaluation presented in Inflation Report III/2013 is created to illustrate the full capabilities of the decomposition framework.
A Comprehensive Analysis of Web-based frequency in Multiword Expression Detection

Multiword expressions (MWEs) are syntactic and/or semantic units in language, where the meaning of whole is limitedly connected to the meanings of the constituting units. The most prominent property that distinguishes MWEs from random word combinations is the recurrence. The recurrence is commonly measured by the occurrence frequencies of the MWE and the constituting words. Though occurrence frequency measures are known to be best in distinguishing MWEs from random combinations, the performance of those measures depend mainly on the quality and size of the data source where frequencies are obtained. The main goal of this study is to provide a detailed analysis on the change in performance of frequency based measures when the traditional frequency source, corpus, is swapped with a massive and dynamic data source, the World Wide Web. In order to use the web as a frequency source, the constituting words and word combinations are queried among a popular search engine, and the number of results for each query is accepted to be web-based frequency for the regarding word/word combination. In this study, the web-based frequencies are employed in three different MWE detection-related experiments utilizing a Turkish data set. In first group of experiments, the individual performances of 20 well-known frequency metrics in ranking/sorting MWE candidates based on their tendency to be a MWE is examined. Secondly, the most successful frequency metrics are determined by a feature selection method: filtering. Lastly, MWE detection is accepted to be a classification problem. Eight supervised methods are applied in order to show the combined performance of frequency metrics when the frequency is obtained from web. In all experiments, the performance of web-based frequencies in identification of MWEs is compared to the performance of traditional corpus based frequencies. The experimental results showed that the use of web-based frequency in identification of MWEs reveals promising results.
Testing the value of TRIZ and its derivatives for knowledge transfer in problem solving attempts by multidisciplinary teams

The design process of ever more complex products requires an increasing amount of knowledge originating in ever more distant domains of expertise. However, in order to make the knowledge transfer (KT) process more effective, researchers ask for processes which foster the transformation and translation of knowledge. In this respect, KT approaches which are based on the systematic use of electronic databases have their limits. Therefore we claim that there is a need for a framework capable of facilitating multidirectional knowledge sharing and thus knowledge transfer during face-to-face working sessions. We think that the well recognized performance of TRIZ and its derivatives in technological problem solving can be transferred to problem identification, modeling and solving in other domains like life sciences. Thus the said methodologies could contribute significantly to innovative product design by linking problems to solutions in distant domains. In this article, we report on a large scale experiment to test this assumption and present some interesting findings on the influence of group composition and methodology on KT during problem solving attempts by multidisciplinary teams.
Comparative Nucleotide and Amino Acid Sequences of the Envelope Protein of Five Hepatitis C Viral cDNA Clones Isolated from HCV Infected Patients in Nagasaki Prefecture

cDNA fragments covering the whole envelope (E) protein region of hepatitis C virus (HCV) have been molecularly cloned by using polymerase chain reaction (PCR) technique from the serum of five HCV infected patients in Nagasaki Prefecture. Their nucleotide and encoded amino acid sequences were determined and compared with other HCV cDNA clones isolated in Japan and the United States. The result showed that HCV cDNA isolates compared in this study were roughly divided into two subgroups; Japanese subgroup and USA subgroup. All HCV cDNA isolates in Nagasaki Prefecture were included in the former subgroup. Amino acid homology analysis showed that conformation of the E protein was relatively conserved among all compared HCV cDNA isolates, indicating that the E protein may be under structural or functional constraint.