Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors that affect the acceptance of new technologies in the workplace: a cross case analysis between two universities

The introduction of a new IT application within an organisation represents change, and the acceptance of such change starts with the individual end users since they are the ones that often resist the newly introduced IT. With the use of survey, this research identified the factors that affect the acceptance of new technologies in the workplace in order to understand better how end-users can influence the successful introduction of IT in academic institutions. We used one Higher Education Institution (HEI) in Hong Kong and one HEI in the UK in order to gather our data and cross analyse the differences between the two institutions. Our research showed that the staff at both universities have a high Behavioral Intention (BI) to use new technologies. However, there was no significant difference between the two universities, which meant these dimensions had no effect on the staff who worked at these universities.
The role of Central Banks in supporting economic growth and creation of productive employment: The case of Pakistan

This study explores different facets of the evolving structure, functions and conduct of Pakistan’s central bank or the State Bank as it is usually called. The study commences by offering an overview of the country’s macroeconomic, labour market and social indicators and evaluates how they have evolved over time. It attempts to gauge the conduct of the State Bank through its response to moments of economic significance faced by the country in the last two and a half decades. Specific attention is paid to the conduct of monetary policy, the role of the State Bank in channelling investment resources to priority sectors and its coordination with fiscal policy.
Automatic concept type identification from learning resources

The objective of any tutoring system is to provide a meaningful learning to the learner. Therefore an automated tutoring system should be able to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. This paper addresses the problem of identifying defined concepts and prerequisite concepts from learning resources in html format. In this paper a supervised machine learning approach was taken to address the problem, based on linguistic features which enclose contextual information and stylistic features such as font size and font weight. This paper shows that contextual information in addition to format information can give better results when used with the SVM classifier than with the (LP)2 algorithm.
The Role of Kinin Receptors in Preventing Neuroinflammation and Its Clinical Severity during Experimental Autoimmune Encephalomyelitis in Mice

Background Multiple sclerosis (MS) is a demyelinating and neuroinflammatory disease of the human central nervous system (CNS). The expression of kinins is increased in MS patients, but the underlying mechanisms by which the kinin receptor regulates MS development have not been elucidated. Methodology/Principal Findings Experimental autoimmune encephalomyelitis (EAE) was induced in female C57BL/6 mice by immunization with MOG35–55 peptide emulsified in complete Freund's adjuvant and injected with pertussis toxin on day 0 and day 2. Here, we report that blockade of the B1R in the induction phase of EAE markedly suppressed its progression by interfering with the onset of the immune response. Furthermore, B1R antagonist suppressed the production/expression of antigen-specific TH1 and TH17 cytokines and transcription factors, both in the periphery and in the CNS. In the chronic phase of EAE, the blockade of B1R consistently impaired the clinical progression of EAE. Conversely, administration of the B1R agonist in the acute phase of EAE suppressed disease progression and inhibited the increase in permeability of the blood-brain barrier (BBB) and any further CNS inflammation. Of note, blockade of the B2R only showed a moderate impact on all of the studied parameters of EAE progression. Conclusions/Significance Our results strongly suggest that kinin receptors, mainly the B1R subtype, play a dual role in EAE progression depending on the phase of treatment through the lymphocytes and glial cell-dependent pathways.
High‐affinity LPS binding domain(s) in recombinant factor C of a horseshoe crab neutralizes LPS‐induced lethality

SSCrFCES is a biologically active, recombinant fragment of factor C, which is the endo‐toxin‐sensitive serine protease of the LAL coagulation cascade. The ~38 kDa protein represents the LPS binding domain of factor C. A novel secretory signal directs the secretion of SSCrFCES into the culture supernatant of Drosophila cells, and hence it is readily purified. By differential ultrafiltration followed by preparative isoelectric membrane electro‐phoresis, SSCrFCES was purified as an isoelectrically homogeneous and stable monomeric protein. The ability of SSCrFCES to bind lipid A was analyzed using an ELISA‐based assay as well as surface plas‐mon resonance. SSCrFCES exhibits high positive cooperativity of binding to two or three lipid A molecules, with a Hill's coefficient of 2.2. The 50% endotoxin‐neutralizing concentration of SSCrFCES against 200 EU of endotoxin is ~0.069 μΜ, suggesting that SSCrFCES is an effective inhibitor of LAL coagulation cascade. Although partially attenuated by human serum, as little as 1 μΜ of SSCrFCES inhibits the LPS‐induced secretion of hTNF‐α and hIL‐8 by THP‐1 and human peripheral blood mono‐nuclear cells with greater potency than polymyxin B. SSCrFCES is noncytotoxic, with a clearance rate of 4.7 ml/min. The L.D. 90 of SSCrFCES for LPS lethality is achieved at 2 μΜ. These results demonstrate the endotoxin‐neutralizing capability of SSCrFCES in vitro and in vivo and its potential use for the treatment of endotoxin‐induced septic shock. —Tan, N. S. , Ho, B. , Ding, J. L. High‐affinity LPS binding domain(s) in recombinant factor C of a horseshoe crab neutralizes LPS‐induced lethality. FASEB J. 14, 859–870 (2000)
Similarity-based method for reduction of fuzzy rules

Fuzzy Similarity Measures (FSMs) are widely used for comparison of fuzzy sets, as well as fuzzy rules. A multitude of different FSMs have been proposed so far. It is not straightforward to identify a single FSM that is the most suitable for a given task. In this paper, we investigate suitability of a few FSMs for the problem of reduction of number of rules for an image segmentation process. We use Dirichlet-based approach to generate fuzzy sets that are further used for construction of fuzzy if-then rules. We analyze similarity of these rules and select a specified number of rules for image segmentation purposes. We applied this approach to two different images.
PatchTable

This paper presents a data structure that reduces approximate nearest neighbor query times for image patches in large datasets. Previous work in texture synthesis has demonstrated real-time synthesis from small exemplar textures. However, high performance has proved elusive for modern patch-based optimization techniques which frequently use many exemplar images in the tens of megapixels or above. Our new algorithm, PatchTable, offloads as much of the computation as possible to a pre-computation stage that takes modest time, so patch queries can be as efficient as possible. There are three key insights behind our algorithm: (1) a lookup table similar to locality sensitive hashing can be precomputed, and used to seed sufficiently good initial patch correspondences during querying, (2) missing entries in the table can be filled during pre-computation with our fast Voronoi transform, and (3) the initially seeded correspondences can be improved with a precomputed k-nearest neighbors mapping. We show experimentally that this accelerates the patch query operation by up to 9× over k-coherence, up to 12× over TreeCANN, and up to 200× over PatchMatch. Our fast algorithm allows us to explore efficient and practical imaging and computational photography applications. We show results for artistic video stylization, light field super-resolution, and multi-image editing.
Summary on Several Key Techniques in 3D Geological Modeling

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized.
The Shape of nonAbelian D branes

We evaluate bulk distribution of energies, pressures and various D-brane/F-string charges generated by nontrivial matrix configurations in nonabelian D-brane effective field theories, using supergravity source density formulas derived originally in Matrix theory. Off-diagonal elements of worldvolume nonabelian fields, especially transverse scalar fields, induce various interesting bulk structures exhibiting the shape of branes. First, we study the energy distribution of string-brane networks generated in the bulk by the Yang-Mills monopoles and the 1/4 BPS dyons, and confirm force balance of them. An application to the Yang-Mills description of recombination of intersecting D-branes gives results indicating presence of the tachyon matter. Second, we analyse the shape of fuzzy D-branes given by nonabelian scalar fields which are mutually noncommutative. We employ fuzzy S2, fuzzy S4 and fuzzy cylinder/supertube as matrix configurations of N D0-branes representing higher dimensional noncommutative D-branes. We find that in the continuum (large-N) limit the D-brane charge distributions become in the expected shape of a sphere or a cylinder with an infinitesimal thickness. However, the distributions found for finite N are difficult to interpret, which leaves a puzzle for a possible dual description in terms of higher dimensional D-branes. A resolution is provided with use of an ordering ambiguity in the charge density formulas.
Iterated neighbor-embeddings for image super-resolution

We propose an exemplar-based super-resolution algorithm based on sparsity constrained neighbor-embeddings of local image patches. We extract exemplar patch pairs from as little as the given low-resolution image, and we rely on local geometric similarities of low-and high-resolution patch spaces. While sparsely coding the local geometry with a greedy patch selection method, we refine our solution by iteratively updating the obtained high-resolution image. We finally apply an adaptive back-projection to ensure the global consistency. Our experimental results indicate promising performance on synthesizing natural looking textures and sharp edges when compared to other super-resolution methods from the literature.
Missing template estimation for user-assisted music transcription

For a user-assisted music transcription system in which the user is asked to label some notes for each instrument in the recording, we investigate ways to limit the amount of information the user has to provide. Different methods are proposed and experimentally compared that enable the estimation of template spectra at pitch positions that have not been annotated by the user, in order to derive a full set of instrument templates that can be used within a non-negative matrix factorisation framework. A set of error metrics is presented that enables the evaluation of the NMF gain matrix. The results show that purely data-driven methods outperform more refined instrument models when the user annotates notes at many different pitches for each instrument. When notes are labelled at a smaller number of different pitches, the highest accuracies are obtained using pre-stored instrument templates that are adapted to the instruments in the mixture.
Improving adherence to acne treatment: the emerging role of application software

Objective To examine recent studies on the effect of mobile and electronic (ME)-health technology on adherence to acne treatment. Background With emerging use of ME-health technology, there is a growing interest in evaluating the effectiveness of the tools on medication adherence. Examples of ME-health technology-based tools include text message-based pill reminders and Web-based patient education. Methods MEDLINE, Cochrane Library, and Web of Science were searched for articles on adherence to acne treatment published through November 2013. A combination of search terms such as “acne” and “adherence” or “compliance” were used. Results Adherence to oral acne medication was higher than for topical acne medication. The frequency of office visits was also an influencing factor for acne treatment adherence. The telephone-based reminders on a daily basis did not improve acne patients’ medication adherence, whereas the Web-based educational tools on a weekly basis had a positive effect on medication adherence in acne treatment. Conclusion In using ME-health interventions, factors such as medication dosage forms, frequency of intervention, and patients’ preferences should be taken into consideration. Developing disease-specific text message reminders may be helpful to increase adherence rates. In addition, a combination of text message reminders with another type of intervention may improve medication adherence.
Integrating Various Resources for Gene Name Normalization

The recognition and normalization of gene mentions in biomedical literature are crucial steps in biomedical text mining. We present a system for extracting gene names from biomedical literature and normalizing them to gene identifiers in databases. The system consists of four major components: gene name recognition, entity mapping, disambiguation and filtering. The first component is a gene name recognizer based on dictionary matching and semi-supervised learning, which utilizes the co-occurrence information of a large amount of unlabeled MEDLINE abstracts to enhance feature representation of gene named entities. In the stage of entity mapping, we combine the strategies of exact match and approximate match to establish linkage between gene names in the context and the EntrezGene database. For the gene names that map to more than one database identifiers, we develop a disambiguation method based on semantic similarity derived from the Gene Ontology and MEDLINE abstracts. To remove the noise produced in the previous steps, we design a filtering method based on the confidence scores in the dictionary used for NER. The system is able to adjust the trade-off between precision and recall based on the result of filtering. It achieves an F-measure of 83% (precision: 82.5% recall: 83.5%) on BioCreative II Gene Normalization (GN) dataset, which is comparable to the current state-of-the-art.
Thermodynamic modelling predicts energetic bottleneck for seabirds wintering in the northwest Atlantic

SUMMARY Studying the energetics of marine top predators such as seabirds is essential to understand processes underlying adult winter survival and its impact on population dynamics. Winter survival is believed to be the single most important life-history trait in long-lived species but its determinants are largely unknown. Seabirds are inaccessible during this season, so conventional metabolic studies are extremely challenging and new approaches are needed. This paper describes and uses a state-of-the-art mechanistic model, Niche Mapper™, to predict energy expenditure and food requirements of the two main seabird species wintering in the northwest Atlantic. We found that energy demand increased throughout the winter phase in both species. Across this period, mean estimated daily energy requirements were 1306 kJ day–1 for Brünnich's guillemots (Uria lomvia) and 430 kJ day–1 for little auks (Alle alle) wintering off Greenland and Newfoundland. Mean estimated daily food requirements were 547 g wet food day–1 for Brünnich's guillemots, and 289 g wet food day–1 for little auks. For both species and both wintering sites, our model predicts a sharp increase in energy expenditure between November and December, primarily driven by climatic factors such as air temperature and wind speed. These findings strongly suggest the existence of an energetic bottleneck for North Atlantic seabirds towards the end of the year, a challenging energetic phase which might explain recurrent events of winter mass-mortality, so called `seabird winter wrecks'. Our study therefore emphasizes the relevance of thermodynamics/biophysical modelling for investigating the energy balance of wintering marine top predators and its interplay with survival and population dynamics in the context of global change.
Identification of FRA-1 as a novel player in pancreatic cancer in cooperation with a MUC1: ERK signaling axis

The MUC1 glycoprotein is overexpressed and aberrantly glycosylated in >90% of pancreatic ductal adenocarcinoma cases and impacts tumor progression by initiating downstream signaling through phosphorylation of its cytoplasmic tail. Previous studies have demonstrated that MUC1 alters expression of known targets of activator protein 1 (AP-1); however, no studies have evaluated the precise impact of MUC1 signaling on the activity and formation of AP-1. Given the known role of these proteins in modulating migration, invasion, and tumor progression, we explored the effects of MUC1 on AP-1 dimer formation and function. We determined that MUC1 increased the protein levels of c-Jun, the major component of AP-1, and promoted dimerization of c-Jun with the Fos-protein FRA-1. We demonstrate that FRA-1 acts as a potent mediator of migration and invasion in a manner that is modulated by signals through MUC1, which acts as a dominant regulator of specific AP-1 and FRA-1 target genes. Our results provide the first in vivo evidence of a FRA-1 mediated expression profile that impacts pancreatic tumor growth properties. In summary, we show that MUC1 enhancement of ERK activation influences FRA-1 activity to modulate tumor migration, invasion and metastasis in a subset of pancreatic cancer cases.
Exchange rate misalignments and economic growth: A threshold panel approach

This study deals with the link between exchange rate misalignments (ERM) and economic growth, for a large sample of advanced and emerging economies on the period 1982-2010. The estimation of equilibrium exchange rate (EER) is based on a FEER approach. The relation "misalignments-growth" is estimated using a PSTR (Panel Smooth Transition Regression) and GMM models. Our main results show that the impact of ERM on economic growth is nonlinear and asymmetric. An overvaluation has a negative impact on growth while an undervaluation sustains growth until an estimated threshold (15.5% for emerging countries and 9% for developed countries). The coefficient is weaker for emerging countries (0.02) than for developed countries (0.08). Due to non linearity and coefficient's value, the undervaluation has a positive effect on growth even beyond the threshold. We also demonstrate that the impact of ERM on growth is positive only in the case of small undervaluation for emerging countries. However, concerning developed countries, the undervaluation have a positive effect on growth even beyond the estimated threshold.
Performance Implications of Corporate Strategic Behavior of Firms in An Emerging Economy during Economic Liberalization

Emerging economies, which have been charting a new path of development, popularly known as economic liberalization, have drawn increasing attention of the strategy scholars. A growing body of literature is emerging to record how firms from these economies transform themselves to adapt to the emerging situation. This paper adds to this body of knowledge by presenting an analysis of the corporate strategic behaviour of firms in India, a giant emerging economy undergoing economic reforms over the last one decade. Based on existing theories a multivariate model has been developed to explore the link between various corporate strategy dimensions and performance. The model has also been empirically verified in LISREL framework using primary and secondary data for 111 firms mainly belonging to the list of top 500 firms in India. It is observed that strategy of scale expansion seems to be the most effective corporate strategic response. In the absence of any significant negative impact of greater degree of diversification and higher diversity of operation on performance, unfocussed strategy and unrelated diversification per se is not be detrimental to firms. However, managers need to be selective in diversification moves for superior performance during the early years of economic liberalization.
THE CASE FOR MARRIAGE

Marriage is one of the greatest things going. In a new book entitled The Case for Marriage, Linda Waite and Maggie Gallagher argue convincingly, and against conventional wisdom, that married people are “happier, healthier, and better off financially. ” Amazing isn’t it. We are now discovering in popular culture what many of us already know. God Knows Best! Yes, even scientific research is now vindicating the Creator’s idea of marriage and the family. For example, when we examine evidence on sex we discover God knows best.
ODESGS framework, knowledge-based markup for semantic grid services

The convergence of the Semantic Web and Grid technologies has resulted in the Semantic Grid. The Semantic Grid should be service-oriented, as the Grid is, so the formal description of Grid Services (GS) turns to be a crucial issue. In this paper we present our approach for this issue. ODESGS Framework will enable the annotation of all the aspects of a GS and the design, discovery and composition Semantic Grid Services (SGS).
Design of a K-band low insertion loss variation phase shifter using 0.18-µm CMOS process

This paper demonstrates a k-band low insertion loss variation phase shifter with over 330° continuously phase tuning range from 21–25GHz in standard 0.18-µm CMOS technology. This phase shifter is composed of a 180 ° continuously phase tuning range reflection type phase shifter (RTPS) and a 180° discrete switch type phase shifter (STPS). The measured phase shift range is 336° with low loss variation of 1.3dB at 22GHz and the maximum insertion loss is 16 dB at 22GHz. To the best of authors' knowledge, the MMIC is the lowest insertion loss variation phase shifter in CMOS technology at 22GHz.
Identifiability and algebraic identification of time delay systems

Identifiability and algebraic identification of time delay systems are investigated in this paper. Identifiability results are first presented for linear delay systems described by convolution equations. On-line algorithms are next proposed for both parameters and delay estimation. Based on a distributional technique, these algorithms enable an algebraic and simultaneous estimation by solving a generalized eigenvalue problem. Simulation studies with noisy data and experimental results show the performance of the proposed approach.