Miracle-dz/UniFAR-FTUs huggingface dataset:
Export-market dynamics and the probability of firm closure: Evidence for the UK

This study presents the first empirical analysis of the determinants of firm closure in the UK with an emphasis on the role of export-market dynamics, using panel data for a nationally representative group of firms operating in all-market based sectors during 1997-2003. Our findings show that the probability of closure is (cet. par.) significantly lower for exporters, particularly those experiencing export-market entry and exit. Having controlled for other attributes associated with productivity (such as size and export status), the following factors are found to increase the firmâ€™s survival prospects: higher capital intensity and TFP, foreign ownership, young age, displacement effects (through relatively high rates of entry of firms in each industry), and belonging to certain industries. Interestingly, increased import penetration (a proxy for lower trade costs) leads to a lower hazard rate for exporting entrants and continuous exporters, whilst inducing a higher hazard rate for domestic producers or those that quit exporting.
Market Microstructure Invariance: Empirical Hypotheses

Using the intuition that financial markets transfer risks in business time, “market microstructure invariance” is defined as the hypotheses that the distributions of risk transfers (“bets”) and transaction costs are constant across assets when measured per unit of business time. The invariance hypotheses imply that bet size and transaction costs have specific, empirically testable relationships to observable dollar volume and volatility. Portfolio transitions can be viewed as natural experiments for measuring transaction costs, and individual orders can be treated as proxies for bets. Empirical tests based on a data set of 400,000+ portfolio transition orders support the invariance hypotheses. The constants calibrated from structural estimation imply specific predictions for the arrival rate of bets (“market velocity”), the distribution of bet sizes, and transaction costs.
Improved Parallel Data Mining Policy for Cloud Computing Environments

Cloud computing is an emerging method of shared infrastructure, it is based on Internet-centric, open standards and service, it provides a safe, fast and convenient data storage and network computing services. Cloud computing applications to data mining can provide more and more solutions for massive data mining. Cloud computing is dealing with TB or even PB level mass data, how to obtain valid information from the data will be the key of cloud computing applications. New ideas, methods and algorithms to accomplish more accurate, fast and powerful data mining are needed in addition to obtain the advantage of parallel computing technology to accelerate the speed of data processing.
THE RELATION BETWEEN GEOGRAPHICAL PLACE AND INNOVATIVENESS: THE CASE OF TURKEY

A line of argument in the new growth economy discussions is whether specialization or diversification of economic activity on the geographical scale stimulates innovation. This study explores the relation between innovativeness and different types of geographical concentrations in the case of a developing country. The study addresses the discussion through statistical and econometric analyses using variables such as number of patents, new firm entry and exit at the regional level for the period 1995-2001. The results do not confirm that regions with higher levels of related variety or specialization are more innovative, but instead regions with higher levels of variety are found to be more innovative supporting the diversity thesis.
Enhanced Load Balancing in Clustered Cloud-based Multimedia System

In any typical centralized hierarchy cloud-based multimedia system (CMS) contains a resource manager, cluster heads and clustered servers. All multimedia service requests of clients, first directed to resource manager then it forwards to appropriate cluster head based on the task characteristic and then again cluster head forwards the client task to the one of the its clustered servers. In such a complex CMS, obviously, there are prominent design challenges to overcome load balancing issues and enhance the cost effectiveness in transmitting multimedia content between CMS and clients' systems without exceeding maximum load capacity of cluster server. Unlike to earlier paper works, this research work considered additional real time CMS use cases with performance monitoring at various time periods and modelled them into a mathematical optimization program, which intern to derive a better approach to utilize maximum capacity of servers performance and also ensuring not to exceed the cluster server load. As a result, this enhanced work got resolved runtime issues of load balancing by mathematical derived models. The imitation of intended mathematical optimization programs over CMS multi-server load balancing resulted significantly enhanced.
Vesicoureteral reflux prognosis using artificial neural networks

The advancement in computer technology has reinforced the development of Artificial Neural Networks (ANN), so that they are used in a wide area of application fields. Medicine is one of these fields. ANNs are suitable for disease prognosis since there is no need to provide diagnosis rules to identify the disease, but a set of examples that represents the variations of disease. This study explores the use of various ANN architectures in vesicoureteral reflux (VUR) prognosis. It is resulted that the performance of an ANN with a hidden layer with hyperbolic tangent sigmoid transfer function and an output layer with saturating linear transfer function is remarkably better against other more complicated structures. The proposed ANN prognoses 100% of the pathological cases (true positive). The aim of proposed network is not to replace the specialists, but to assist general physicians and specialists in predicting VUR in order to avoid the unnecessary exposure of children in voiding cystourethrogram.
Descriptor Trends in Texture Classification for Material Recognition

Recent rapid growth in the demand for technology and image investigation in many applications, such as image retrieval systems and Visual Object Categorization (VOC), effective management of these applications has become crucial. Computer vision and its various applications are a primary focus of research. Content-based image retrieval is considered an extremely challenging issue and has remained an open research area. Obviously, the main challenge associated with this kind of research is the gap between the low-level features and the richness of the semantic concept of the human mind. This problem is called the semantic gap. Several methods have been proposed to increase the performance of the system and reduce the semantic gap. These proposed techniques make use of either global or local features or a combination of both global and local features on one side and the visual content and keyword-based retrieval on the other side. However, the aim of this study is to provide a constructive critique of the algorithms used in extracting the low-level features, either globally or locally or as a combination of both. In addition, it identifies the factors that can affect the low-level features that lead to the semantic gap. As well as, proposed a new framework to improve the Gabor filter and the edge histogram limitations. Finally, recommendations are made for the choice of the descriptors used to describe the low-level features, both locally and globally, depending on the area of limitations or drawbacks of the previous state-of-the-art research.
Semantic Web-based Product Lifecycle Management with Protected Interactive 3D Collaboration via Remote Rendering

Product Lifecycle Management (PLM) is the activity of managing products over their lifecycle. The goal is to achieve a total integration of all components to better understand products and their environments and to establish competitive advantages. This thesis proposes the use of semantic web technologies to support PLM. An overview of the semantic web briefly describes the basic building blocks. A review of literature reveals many useful applications from automated question answering to information security. The framework for semantic web-based PLM collaboration is examined and an ontology development process is presented in the context of PLM. Software systems were developed to perform semantic web automation and interactive 3D visualization. Applications of semantic web technologies for information retrieval, step by step process support, and design and manufacturing support are demonstrated. A novel 3D collaboration approach that protects 3D virtual assets via remote rendering and the service of a trusted shared mask broker was developed. The novelty of the proposed approach is that companies can collaborate on shared digital mockups without having to share their 3D models. Instead, only the required views for specific tasks are automatically exchanged, thereby eliminating the need for 3D navigation.
Exact and efficient inference for Partial Bayes problems

Bayesian methods are useful for statistical inference. However, real-world problems can be challenging using Bayesian methods when the data analyst has only limited prior knowledge. In this paper we consider a class of problems, called Partial Bayes problems, in which the prior information is only partially available. Taking the recently proposed Inferential Model approach, we develop a general inference framework for Partial Bayes problems, and derive both exact and efficient solutions. In addition to the theoretical investigation, numerical results and real applications are used to demonstrate the superior performance of the proposed method.
Extremely Rapid X-Ray Flares of TeV Blazars in the RXTE Era

Rapid flares from blazars in very high-energy (VHE) γ-rays challenge the common understanding of jets of active galactic nuclei (AGNs). The same population of ultra-relativistic electrons is often thought to be responsible for both X-ray and VHE emission. We thus systematically searched for X-ray flares at sub-hour timescales of TeV blazars in the entire Rossi X-ray Timing Explorer archival database. We found rapid flares from PKS 2005−489 and S5 0716+714, and a candidate rapid flare from 1ES 1101−232. In particular, the characteristic rise timescale of PKS 2005−489 is less than half a minute, which, to our knowledge, is the shortest among known AGN flares at any wavelengths. The timescales of these rapid flares indicate that the size of the central supermassive black hole is not a hard lower limit on the physical size of the emission region of the flare. PKS 2005−489 shows possible hard lags in its flare, which could be attributed to particle acceleration (injection); its flaring component has the hardest spectrum when it first appears. For all flares, the flaring components show similar hard spectra with , and we estimate the magnetic field strength B ∼ 0.1–1.0 G by assuming synchrotron cooling. These flares could be caused by inhomogeneity of the jets. Models that can only produce rapid γ-ray flares but little synchrotron activity are less favorable.
JEZIK: A Cognitive Translation System Employing a Single, Visible Spectrum Tracking Detector

JEZIK: A Cognitive Translation System Employing a Single, Visible Spectrum Tracking Detector Davor Bzik Department of Electrical and Computer Engineering, BYU Master of Science A link between eye movement mechanics and the mental processing associated with text reading has been established in the past. The pausing of an eye gaze on a specific word within a sentence reflects correctness or fluency of a translated text. A cognitive translation system has been built employing a single, inexpensive web camera without the use of infrared illumination. It was shown that the system translates the text, detects rarely occurring and out-of-context words from eye gaze information, and provides solutions in real time while the user is still reading. The solutions are in form of a translation, definition or synonym for the word in question. The only effort required is that of reading.
A Novel Identification Method of Two Phase Flow Based on LDA Feature Extraction and GRNN in ERT System

Two-phase flow measurement plays an increasingly important role in the real-time, on-line control of industrial processes including fault detection and system malfunction. The flow regime parameter is one of the most important parameters in measurements. This paper proposes a new identification approach for common two phase flow regimes based on Electrical Tomography measurement. LDA feature extraction was employed to extract feature vectors. GRNN was used to train identify the flow regime models. Simulation was carried out for typical flow regimes using the approach. The results show its feasibility, and the results indicate that this method is fast in speed and can identify these flow regimes correctly. Keywords-electrical resistance tomography; flow regime identification; linear discriminant analysis; general regression neural network.
The influence of a Mediterranean diet with and without red wine on the haemostatic and inflammatory parameters of subjects with the metabolic syndrome

This 8 week study examined whether a Mediterranean diet supplemented with red wine, had an acute impact on subjects diagnosed with the metabolic syndrome. Twelve non-smoking subjects with diagnostic criteria of the metabolic syndrome on minimal medication, consumed a Mediterranean-like diet for 4 weeks respectively without and with red wine. The amount of red wine consumed was 250 ml (26 grams of alcohol) per day for male and 180 ml (19 grams) per day for female participants. A nutrigenetic profile for cardiovascular risk factors was performed on each participant. Fasting blood specimens were taken at baseline, after the diet and after the diet with wine interventions for platelet function, procoagulants FVII and FVIII, von Willebrand’s factor, fibrinogen, tissue plasminogen activator, plasminogen activator inhibitor-1, highly sensitive C-reactive protein and oxygen radical absorbance capacity (ORAC). After both periods of diet without wine and with wine, ORAC increased significantly compared to baseline levels. Except for platelet H2O2 fluxes and FVII concentration, none of the haemostatic or inflammatory parameters changed significantly after the intervention periods compared with baseline levels. Genetic risk factors for cardiovascular disease were identified in all study participants and the potential genotypic effects relevant to this study were generally in agreement with expected phenotypic response following the dietary intervention. Our conclusions are that the period of intervention was too short for substantial changes in haemostatic or in inflammatory parameters in subjects who already manifest some changes in their cardiovascular system and who showed diverse genetic profiles underlying increased cardiovascular risk.
Tutors and gatekeepers in sustainability MOOCS

Purpose The purpose of this paper is to explore the nature of interactions between learners in a massive open online course (MOOC), particularly role of the tutors in such interactions. For educators concerned with sustainability literacy, the authors are necessarily both affected by, and effectors of, digital pedagogies. The call for papers for this special issue challenged the authors to consider whether digital pedagogies are “supportive of sustainability or perpetuators of unsustainability”. As might be expected, this question is not a simple binary choice and the authors have chosen to address it indirectly, by considering the nature of interaction in a global, digitally connected community of learners. In particular, the changing role of tutors in these communities, and the possible implications of this change on sustainable literacy, are examined. Design/methodology/approach The authors focus on the “Sustainability for Professionals” massive open online course (MOOC) delivered by the University of Bath on the FutureLearn platform which hosts the “Inside Cancer” MOOC, also from Bath. “Sustainability for Professionals” is pedagogically connectivist, with “Inside Cancer” being more traditional and instructor led. The authors used social network analysis (SNA) for the research. It is a key tool to understand interactions in an online environment and allows quantitative comparison between different networks and thus between courses. In the context of digital pedagogy, the authors used a number of relevant SNA metrics to carry out analysis of MOOC network structures. Findings It was found that MOOCs are different in their network structure but tend to adapt to the subject matter. Digital pedagogies for sustainability result in a qualitative as well as quantitative change in learning where course design affects the learning process and gatekeepers are critical for information flow. These gatekeepers are distinct from tutors in the network. In such a network, tutors’ role is limited to course delivery and verifying, depending on course content, the information within the network. The analysis shows that network learning is dependent on course design and content, and gatekeepers exercise influence over the information within the network. Originality/value This study has implications for sustainability literacy. The authors examined the extent to which patterns of interaction in the network affect the learning process, and how this can help participants engage with the concept of sustainability. They used SNA to explore the nature of interaction between learners in a MOOC, particularly the role of the tutors in mediating such interactions. They also found that tutors can and do take a central role in early runs of the MOOC; however, with the subsequent runs, the removal of tutor nodes has little effect, suggesting that different modes of learning driven by participants are possible in a MOOC community.
Direct versus search engine traffic: An innovative approach to demand analysis in the property market

Purpose - The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research. Design/methodology/approach - In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly-used UK online real-estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation. Findings - The main finding from this study is that GI4S data produce immediate real-time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship. Originality/value - This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.
A classifier model for detecting pronunciation errors regarding the Nasa Yuwe language's 32 vowels

La lengua nasa yuwe tiene 32 vocales, dividas en orales y nasales, lo que lleva a confusiones en las que una vocal es utilizada en lugar de otra. En nasa yuwe esta confusion puede cambiar el significado de las palabras pronunciadas. Con el fin de apoyar el aprendizaje correcto de esta lengua en peligro de extincion se ha desarrollado un conjunto de modelos de clasificacion que permitan detectar tales confusiones de pronunciacion de las 32 vocales. Luego de experimentar con una variedad de clasificadores lineales y no lineales, las tasas de sensibilidad, especificidad y precision que se obtuvieron estan alrededor del 85%. Con los clasificadores entrenados se construyo un prototipo de herramienta de software disenada para apoyar la practica de la correcta pronunciacion de las vocales de esta lengua
Vertical Shipyard: Technology Transfer for Automated Con- and Deconstruction

In this paper, we show that automated con -and deconstruction systems often are complex combinations of horizontal and vertical process, assembly and logistics sequences. The combinations of those horizontal and vertical sequences are exactly what characterize today’s ship production. Shipyard technology is highly advanced and individual, customized ships are built with a high degree of automation. We outline that first attempts have been made by German architects and Japanese construction companies to utilize shipyards, shipyard processes and shipyard automation technology to construct buildings or building elements. Further, we suggest the utilization of South Korean shipyard technology (organization, logistics, processes and automation technology) to support and speed up South Korea’s current R&D in automated building construction by proposing technology transfer from “chaebol” shipyard to construction industry.
Running Title : PGC-1 α isoform and blood flow restriction 1 2 Attenuated PGC-1 α i soforms following endurance exercise with blood flow restriction

26 Introduction: Exercise performed with blood flow restriction simultaneously e nhances 27 the acute responses to both myogenic and mitochondrial pathways with roles in trai ning 28 adaptation. We investigated isoform -specific gene expression of the peroxisome 29 proliferator-activated receptor gamma (PPARG) coactivator 1 and selected target genes 30 and proteins regulating skeletal muscle training adaptation. Methods: 9 healthy, 31 untrained males participated in a randomized, counter -balanced, cross -over design in 32 which each subject completed a bout of low -intensity endurance exercise performed 33 with blood flow restriction (15 min cycling at 40% of VO 2peak, BFR-EE), endurance 34 exercise ( 30 min cycling at 70% of VO2peak, EE) or resistance exercise ( 4 x 10 35 repetitions of leg press at 70% of 1 -repetition maximum, RE) separated by at least one 36 week recovery. A single resting muscle biopsy ( vastus lateralis) was obtained two 37 weeks before the first exercise trial (rest) and 3 h after each boat . Results: Total PGC -1α 38 mRNA abundance, along with all four isoforms, increased above rest with EE only 39 (P<0.05) being higher than BFR -EE (P<0.05). PGC -1α1, 2 and 4 were higher after EE 40 compared to RE (P<0.05). EE also increased VEGF, Hif -1α and MuRF-1 mRNA 41 abundance above rest (P<0.05) while COXIV mRNA expression increased with EE 42 compared to BFR -EE (P<0.05). Conclusion: The attenuated expression of all four PGC 43 1α isoforms when endurance exercise is performed with blood flow restriction suggests 44 this type of exercise provides an insufficient stimulus to activate the signaling p thways 45 governing mitochondrial and angiogenesis responses observed with moderate to high 46 intensity endurance exercise. 47 48
Asymptotic behavior of characteristic function of simple serial rank statistics

Abstract Under suitable assumptions, verified for a wide class of score generating functions including van der Waerden, Willcoxon and Spearman scores, we establishes an asymptotic bound on the characteristic function of serial linear rank statistics. It generalizes the result of van Zwet [13] and constitutes an essential step to the elaboration an Edgeworth expansion for distribution function of these statistics.