Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-level Trajectory-Based Routing Protocol for Vehicular Ad Hoc Networks in Freeway and Manhattan Environments

This paper focuses on the routing protocol issue in two important environments for Vehicular Ad Hoc Networks (VANET): Manhattan and the Freeway. A novel protocol called Two-level Trajectory Based Routing (TTBR) protocol is proposed. Deterministic vehicles movement permits advantage to be taken of the map info to build a specific local trajectory to reach the destination node. However, in order to offer network scalability also a high level cell-based trajectory is applied to have a coarse knowledge of the cell where the destination node is moving. Our proposal needs Peer Servers and Grid subdivision of the space. Simulation results were assessed to show the improvements and scalability offered by TTBR in comparison with other Ad Hoc networks protocols such as AODV and GPSR. Performance Evaluation was evaluated in terms of Normalized Control Overhead and Data Packet Delivery Ratio. TTBR is more performing than AODV for a high speed and high density scenario for both the Manhattan and Freeway scenarios.
Capstone Report-Project NoScope

This project report covers the development of a computational 3D microscope, NoScope. Using tomographic and light field algorithms, we present a method to reconstruct 3D volumes of microscopic samples taken with a lensless sensor. Business and intellectual property strategies for commercializing NoScope are detailed in the first three sections. The remaining sections highlight the project’s technical accomplishments and methods. Capstone Report Project NoScope
Islamic and Conventional Banking in the Nineties: A Comparative Study

There is a serious lack of empirical studies on Islamic banking. This paper attempts to fill that gap to some extent. Using data for the 1990-98 period, several hypotheses and common perceptions about the practice of Islamic banking have been tested. The performance of Islamic banks has been evaluated using both trend and ratio analyses. For this purpose, some objective “benchmarks” for various ratios have been developed for the first time. The performance of Islamic banks has also been compared with a ‘control group’ of conventional banks. It has been found that in general Islamic banks have done fairly well during the period under study.
The remote monad design pattern

Remote Procedure Calls are expensive. This paper demonstrates how to reduce the cost of calling remote procedures from Haskell by using the remote monad design pattern, which amortizes the cost of remote calls. This gives the Haskell community access to remote capabilities that are not directly supported, at a surprisingly inexpensive cost. We explore the remote monad design pattern through six models of remote execution patterns, using a simulated Internet of Things toaster as a running example. We consider the expressiveness and optimizations enabled by each remote execution model, and assess the feasibility of our approach. We then present a full-scale case study: a Haskell library that provides a Foreign Function Interface to the JavaScript Canvas API. Finally, we discuss existing instances of the remote monad design pattern found in Haskell libraries.
"Trained ears" and "correlation coefficients": A social science perspective on sonification

This paper presents a social science perspective on the field of sonification research. Adopting a perspective informed by constructivist science and technology studies (STS), the paper begins by arguing why sonification is an interesting case study to reconsider the role of sensory representation in scientific practice, and in particular the creation of credibility in science. It then focuses on a debate in which the meaning of objectivity is negotiated within the sonification community, showing that different notions of objectivity and scientific quality co-exist within the community, which are linked to different research questions being asked with the sonifications, different users that are envisaged for the sonifications, and different disciplinary backgrounds of the sonification researchers.
Mucosal Vaccine Development Based on Liposome Technology

Immune protection against infectious diseases is most effective if located at the portal of entry of the pathogen. Hence, there is an increasing demand for vaccine formulations that can induce strong protective immunity following oral, respiratory, or genital tract administration. At present, only few mucosal vaccines are found on the market, but recent technological advancements and a better understanding of the principles that govern priming of mucosal immune responses have contributed to a more optimistic view on the future of mucosal vaccines. Compared to live attenuated vaccines, subcomponent vaccines, most often protein-based, are considered safer, more stable, and less complicated to manufacture, but they require the addition of nontoxic and clinically safe adjuvants to be effective. In addition, another limiting factor is the large antigen dose that usually is required for mucosal vaccines. Therefore, the combination of mucosal adjuvants with the recent progress in nanoparticle technology provides an attractive solution to these problems. In particular, the liposome technology is ideal for combining protein antigen and adjuvant into an effective mucosal vaccine. Here, we describe and discuss recent progress in nanoparticle formulations using various types of liposomes that convey strong promise for the successful development of the next generation of mucosal vaccines.
Drug Metabolism and Transport During Pregnancy: How Does Drug Disposition Change during Pregnancy and What Are the Mechanisms that Cause Such Changes?

There is increasing evidence that pregnancy alters the function of drug-metabolizing enzymes and drug transporters in a gestational-stage and tissue-specific manner. In vivo probe studies have shown that the activity of several hepatic cytochrome P450 enzymes, such as CYP2D6 and CYP3A4, is increased during pregnancy, whereas the activity of others, such as CYP1A2, is decreased. The activity of some renal transporters, including organic cation transporter and P-glycoprotein, also appears to be increased during pregnancy. Although much has been learned, significant gaps still exist in our understanding of the spectrum of drug metabolism and transport genes affected, gestational age–dependent changes in the activity of encoded drug metabolizing and transporting processes, and the mechanisms of pregnancy-induced alterations. In this issue of Drug Metabolism and Disposition, a series of articles is presented that address the predictability, mechanisms, and magnitude of changes in drug metabolism and transport processes during pregnancy. The articles highlight state-of-the-art approaches to studying mechanisms of changes in drug disposition during pregnancy, and illustrate the use and integration of data from in vitro models, animal studies, and human clinical studies. The findings presented show the complex inter-relationships between multiple regulators of drug metabolism and transport genes, such as estrogens, progesterone, and growth hormone, and their effects on enzyme and transporter expression in different tissues. The studies provide the impetus for a mechanism- and evidence-based approach to optimally managing drug therapies during pregnancy and improving treatment outcomes.
Multi-level convolutional channel features for content-based image retrieval

An effective content-based image retrieval (CBIR) system depends on the discriminative feature which represents an image. In this work, we explore deep convolutional features for a CBIR system. We first show the effectiveness of deep convolutional channel features for a CBIR system. Then we introduce a Multi-Level Pooling method (MLP) to obtain object-aware features from convolutional layers and finally the features extracted from different layers are incorporated to a short representation vector. Through multiple experiments, we show that our approach can achieve state-of-art results on several benchmark retrieval datasets.
A Lagrangian subgrid-scale model with dynamic estimation of Lagrangian time scale for large eddy simulation of complex flows

The dynamic Lagrangian averaging approach for the dynamic Smagorinsky model for large eddy simulation is extended to an unstructured grid framework and applied to complex flows. The Lagrangian time scale is dynamically computed from the solution and does not need any adjustable parameter. The time scale used in the standard Lagrangian model contains an adjustable parameter θ. The dynamic time scale is computed based on a “surrogate-correlation” of the Germano-identity error (GIE). Also, a simple material derivative relation is used to approximate GIE at different events along a pathline instead of Lagrangian tracking or multi-linear interpolation. Previously, the time scale for homogeneous flows was computed by averaging along directions of homogeneity. The present work proposes modifications for inhomogeneous flows. This development allows the Lagrangian averaged dynamic model to be applied to inhomogeneous flows without any adjustable parameter. The proposed model is applied to LES of turbulent channel .
Longitudinal changes in the nature, severity and frequency of COPD exacerbations

Exacerbations are an important feature and outcome measure in chronic obstructive pulmonary disease (COPD), but little is known about changes in their severity, recovery, symptom composition or frequency over time. In this study 132 patients (91 male; median age 68.4 yrs and median forced expiratory volume in one second (FEV1) 38.4% predicted) recorded daily symptoms and morning peak expiratory flow. Patients were monitored for a median of 918 days and 1,111 exacerbations were identified. Patients with severe COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) category III, n=38) had an annual exacerbation frequency of 3.43·yr−1, 0.75·yr−1 higher than those with moderate COPD (GOLD II, n=94). Exacerbation frequency did not change significantly during the study. At exacerbation onset, symptom count increased to 2.23, relative to a baseline of 0.36 set 8–14 days previously, and this increase rose by 0.05·yr−1. Recovery to baseline levels in symptoms and FEV1 took longer (0.32 and 0.55 days·yr−1). Sputum purulence at exacerbation became more prevalent over time by 4.1%·yr−1 from an initial value of 17%. The results of this study suggest that over time, individual patients have more symptoms during exacerbations, with an increased chance of sputum purulence and longer recovery times.
Placing microalgae on the biofuels priority list: a review of the technological challenges

Microalgae provide various potential advantages for biofuel production when compared with ‘traditional’ crops. Specifically, large-scale microalgal culture need not compete for arable land, while in theory their productivity is greater. In consequence, there has been resurgence in interest and a proliferation of algae fuel projects. However, while on a theoretical basis, microalgae may produce between 10- and 100-fold more oil per acre, such capacities have not been validated on a commercial scale. We critically review current designs of algal culture facilities, including photobioreactors and open ponds, with regards to photosynthetic productivity and associated biomass and oil production and include an analysis of alternative approaches using models, balancing space needs, productivity and biomass concentrations, together with nutrient requirements. In the light of the current interest in synthetic genomics and genetic modifications, we also evaluate the options for potential metabolic engineering of the lipid biosynthesis pathways of microalgae. We conclude that although significant literature exists on microalgal growth and biochemistry, significantly more work needs to be undertaken to understand and potentially manipulate algal lipid metabolism. Furthermore, with regards to chemical upgrading of algal lipids and biomass, we describe alternative fuel synthesis routes, and discuss and evaluate the application of catalysts traditionally used for plant oils. Simulations that incorporate financial elements, along with fluid dynamics and algae growth models, are likely to be increasingly useful for predicting reactor design efficiency and life cycle analysis to determine the viability of the various options for large-scale culture. The greatest potential for cost reduction and increased yields most probably lies within closed or hybrid closed–open production systems.
Radarsat Geomatic System for Oil Slick Trajectory Forecasting

Abstract This study presents work to utilize RADARSAT SAR image for prediction oil slick trajectory movement. The entropy and LEE algorithms used to detect oil slick. The Doppler frequency shift and quasi‐linear model was used to simulate a current pattern from RADARSAT image. The Fay's algorithm of oil slick spreading was developed based on a Doppler frequency shift model. Thus, the study shows that entropy and LEE algorithms discriminated the oil slick from the surrounding water features. The quasi‐linear model shows that the current pattern can be simulated from single RADARSAT image. The oil slick trajectory model shows that after 48 hrs, the oil slick parcels deposited along the coastal waters. It can be concluded that RADARSAT single data can be used as tool for oil slick forecast. The oil slick trajectory model is integration of several algorithms. These algorithms were a combination of entropy algorithm, Lee algorithm, quasi‐linear model and spreading models. It can be said that the Fay's algorithm can be developed based on Doppler Frequency shift models to simulate oil slick trajectory movement from single RADARSAT image.
Round-trip semantics with sztakipedia and DBpedia spotlight

We describe a tool kit to support a knowledge-enhancement cycle on the Web. In the first step, structured data which is extracted from Wikipedia is used to construct automatic content enhancement engines. Those engines can be used to interconnect knowledge in structured and unstructured information sources on the Web, including Wikipedia itself. Sztakipedia-toolbar is a MediaWiki user script which brings DBpedia Spotlight and other kinds of machine intelligence into the Wiki editor interface to provide enhancement suggestions to the user. The suggestions offered by the tool focus on complementing knowledge and increasing the availability of structured data on Wikipedia. This will, in turn, increase the available information for the content enhancement engines themselves, completing a virtuous cycle of knowledge enhancement.
Simulations of dual morphology in spiral galaxies

Gas and stars in spiral galaxies are modelled with a combination of hydrodynamic and N-body techniques. The sim- ulations reveal morphological dierences mirroring the dual morphologies seen in B and K 0 band observations of many spiral galaxies: gaseous images have tighter pitch angles, are more asymmetric, more flocculent and more likely to have multiple arms. Morphological decoupling increases as the stellar arm-interarm contrast and the Q parameter fall. The flocculence of a galaxy is quantified by decomposing the images into logarithmic spirals and defining a parameter closely related to the uniformity of the resulting 2D Fourier spectrum. Thus, a significant amount of morphological decoupling in spiral galaxies is shown to be due to the dierence in the dynamics of stars and gas, rather than dust, star formation or galaxy interactions.
The Neighbour Search approach to construct Pareto sets of multi-objective Markov Decision Processes

The Neighbour Search (NS) algorithm, is an iterative method for constructing Pareto sets of multi-dimensional polytopes. A NS iteration consists in two steps: Edges Exploration and Neighbour Detection. Edges Exploration takes a Pareto vertex and determines all Pareto edges connecting such a Pareto vertex to its neighbours. Each neighbour is again a Pareto vertex that is obtained by Neighbour Detection. The procedure continues until all Pareto vertices are explored. The purpose of this paper is to describe in detail the application of NS to Markov Decision Processes (MDPs) with N discounted objectives. Novel numeric techniques are herein developed to effectively adapt Edges Exploration and Neighbour Detection to the MDPs characteristics. Edges Exploration consists of solving a problem of redundancy removal for systems of linear inequalities in N dimensions; the number of inequalities is equivalent to the size of the MDP. Neighbour Detection is performed either by Direct Neighbour Search (DNS) or by Cross Neighbour Search (CNS). The former requires the Bellman equation to be solved, even though with a reduced action set. The latter does not require the Bellman equation to be solved, and is computationally linear in the size of the MDP, and thus more efficient than DNS. However, CNS requires conditions that are not always fulfilled, whereas DNS is always applicable. Experimental results suggest that conditions for CNS to be applicable are actually satisfied for the most of NS iterations. In Gianluca Dorini Department of Environmental Engineering, Technical University of Denmark, Miljvej 113, DK-2800 Kongens Lyngby, Denmark E-mail: gido@env.dtu. dk Dragan Savić Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, Harrison Building, North Park Road,Exeter EX4 4QF UK, E-mail: D.Savic@exeter.ac. uk
Design and simulation of an optimized slotted patch antenna for RFID reader applications

In this work, new form of patch antenna for RFID reader applications in the microwave band has been proposed. The patch is a perfect electrical conductor, excited by a 50 ohms microstrip line, set on an Epoxy FR-4 substrate. The first results were obtained on a basic structure of a rectangular patch antenna adapted with notches. These results have been improved by introducing slots in the basic structure. The structure design, optimization and simulation results analysis were performed under electromagnetic simulators HFSS and CST, and focused on the return loss, bandwidth, gain and directivity.
Aminoacyl-tRNA-Charged Eukaryotic Elongation Factor 1A Is the Bona Fide Substrate for Legionella pneumophila Effector Glucosyltransferases

Legionella pneumophila, which is the causative organism of Legionnaireś disease, translocates numerous effector proteins into the host cell cytosol by a type IV secretion system during infection. Among the most potent effector proteins of Legionella are glucosyltransferases (lgt's), which selectively modify eukaryotic elongation factor (eEF) 1A at Ser-53 in the GTP binding domain. Glucosylation results in inhibition of protein synthesis. Here we show that in vitro glucosylation of yeast and mouse eEF1A by Lgt3 in the presence of the factors Phe-tRNAPhe and GTP was enhanced 150 and 590-fold, respectively. The glucosylation of eEF1A catalyzed by Lgt1 and 2 was increased about 70-fold. By comparison of uncharged tRNA with two distinct aminoacyl-tRNAs (His-tRNAHis and Phe-tRNAPhe) we could show that aminoacylation is crucial for Lgt-catalyzed glucosylation. Aminoacyl-tRNA had no effect on the enzymatic properties of lgt's and did not enhance the glucosylation rate of eEF1A truncation mutants, consisting of the GTPase domain only or of a 5 kDa peptide covering Ser-53 of eEF1A. Furthermore, binding of aminoacyl-tRNA to eEF1A was not altered by glucosylation. Taken together, our data suggest that the ternary complex, consisting of eEF1A, aminoacyl-tRNA and GTP, is the bona fide substrate for lgt's.
The neurobiology of collective action

This essay introduces a neurologically-informed mathematical model of collective action (CA) that reveals the role for empathy and distress in motivating costly helping behaviors. We report three direct tests of model with a key focus on the neuropeptide oxytocin as well as a variety of indirect tests. These studies, from our lab and other researchers, show support for the model. Our findings indicate that empathic concern, via the brain's release of oxytocin, is a trigger for CA. We discuss the implications from this model for our understanding why human beings engage in costly CA.
A social epistemology for educational administration and leadership

ABSTRACT As a field of knowledge production, educational administration and leadership scholars do a substantial amount of talking past one another. These parallel monologues are a major issue for the advancement of knowledge. Original contributions can only be made in relation to others. That is, the innovation or significance of scholarship is an act of (social) scientific distinction. This means purposely engaging with the other. In this paper, I argue that the knowledge frontiers of educational administration and leadership are highly fragmented and siloed. I do not, however, see diversity of scholarship as a fatal flaw nor do I argue for a form of knowledge centrism. Rather, my intervention is to propose a social epistemology for moving knowledge claims.
The Influence of Early Experience Upon the Development of Social Behavior in Estrildine Finches

Besides the mother-infant-relationships discussed in a previous paper, experience during the first days or weeks of life has also proved to exert an important influence on adult behavior, mainly upon the determination of sexual preferences, but also on several other aspects of social behavior. The establishment of sexual attachments through early experience is generally referred to as sexual imprinting. Its characteristics have first been compiled by Lorenz (I 935) although the phenomenon of early sexual fixation had been mentioned long before (cf. Gray 1963). Since 1935, numerous experimental investigations have been carried out about sexual imprinting. Most of them have been concerned with precocial birds (for review, see Moltz 1963, Hess 1964, Schutz 1965, 1968, Sluckin 1965, Bateson 1966, o.a.). For altricial birds, material is still very scanty and consists mainly of numerous occasional observations of hand-raised individuals which became imprinted on humans (for review, see Klinghammer 1967). The only experimental investigations with altricial birds are those by Nicolai (1956, 1959, 1964)with Bullfinches Pyrrhula pyrrhula and the parasitic widow birds Viduinae, by Warriner, et a/. {1963) with pigeons and by Klinghammer (I.e.) with Ring Doves Streptopelia risoria and Mourning Doves Zenaidura macroura (Klinghammer I.e.).
Variable ligand- and receptor-binding hot spots in key strains of influenza neuraminidase

Influenza A continues to be a major public health concern due to its ability to cause epidemic and pandemic disease outbreaks in humans. Computational investigations of structural dynamics of the major influenza glycoproteins, especially the neuraminidase (NA) enzyme, are able to provide key insights beyond what is currently accessible with standard experimental techniques. In particular, all-atom molecular dynamics simulations reveal the varying degrees of flexibility for such enzymes. Here we present an analysis of the relative flexibility of the ligand- and receptor-binding area of three key strains of influenza A: highly pathogenic H5N1, the 2009 pandemic H1N1, and a human N2 strain. Through computational solvent mapping, we investigate the various ligand- and receptor-binding “hot spots” that exist on the surface of NA which interacts with both sialic acid receptors on the host cells and antiviral drugs. This analysis suggests that the variable cavities found in the different strains and their corresponding capacities to bind ligand functional groups may play an important role in the ability of NA to form competent reaction encounter complexes with other species of interest, including antiviral drugs, sialic acid receptors on the host cell surface, and the hemagglutinin protein. Such considerations may be especially useful for the prediction of how such complexes form and with what binding capacity.