Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecology and evolution of plant-pollinator interactions.

BACKGROUND Some of the most exciting advances in pollination biology have resulted from interdisciplinary research combining ecological and evolutionary perspectives. For example, these two approaches have been essential for understanding the functional ecology of floral traits, the dynamics of pollen transport, competition for pollinator services, and patterns of specialization and generalization in plant-pollinator interactions. However, as research in these and other areas has progressed, many pollination biologists have become more specialized in their research interests, focusing their attention on either evolutionary or ecological questions. We believe that the continuing vigour of a synthetic and interdisciplinary field like pollination biology depends on renewed connections between ecological and evolutionary approaches. SCOPE In this Viewpoint paper we highlight the application of ecological and evolutionary approaches to two themes in pollination biology: (1) links between pollinator behaviour and plant mating systems, and (2) generalization and specialization in pollination systems. We also describe how mathematical models and synthetic analyses have broadened our understanding of pollination biology, especially in human-modified landscapes. We conclude with several suggestions that we hope will stimulate future research. This Viewpoint also serves as the introduction to this Special Issue on the Ecology and Evolution of Plant-Pollinator Interactions. These papers provide inspiring examples of the synergy between evolutionary and ecological approaches, and offer glimpses of great accomplishments yet to come.
Housing Pathways of Immigrants in the City of Athens: From Homelessness to Homeownership. Considering Contextual Factors and Human Agency

ABSTRACT Against the background of the so-called “new immigration” to Greece starting at the beginning of the 1990s, this paper explores the housing experience of immigrants in the city of Athens and reveals housing pathways that have been overlooked or deliberately concealed. Through the processing of data from the Athens Mortgage Office and through interviews with migrant homeowners, it is shown that despite serious difficulties raised against their spatial and social integration, immigrants managed to escape homelessness, gradually upgrade both their housing conditions and occupancy status, and gain access even to homeownership. Special emphasis is given to key factors that allowed immigrants to achieve upward residential mobility and that relate to specific aspects of the local context and to immigrants’ individual strategies. Urban space emerges as an “opportunity framework” and immigrants as “active agents” who set up successful housing strategies and reverse the supposedly vicious circle of poverty, delinquency and marginality.
A new method of fault tolerance TCP

With the rapid development of Internet, the need of high availability of data services on Internet becomes more urgent. But as one of the most useful protocol on Internet, TCP protocol software can not solve the high availability due to the failure of hardware or software on server/client. In this paper, we propose a new method that can implement fault tolerance TCP to improve the high availability of data transmission. First, we analyze some existing methods of fault tolerant TCP; then based on the characteristic of present server architecture, we put forward our new method of fault tolerant TCP; and lastly, we describe in detail how to implement and test our method. Experimental results show that our fault-tolerant TCP can offer high available and high effective communication support for reliable data service on Internet.
Intelligent tutoring system based on belief networks

This paper proposes a new Intelligent Tutoring System based on Belief networks. The unique features of this system are as follows: 1. The Student model is represented by belief networks which has a Dirichret distribution as a prior, 2. The structure of the student model is constructed from the data-base by maximizing the predict distribution, and 3. Instruction strategies are described as utility functions in decision making theory. Especially, in this paper, the utility function is assumed as the expected learning effects defined by EVII (Expected Value of Instruction Information), and it is shown that it explains sufficiently teacher flexible behavior and interactive instruction process.
SINAI at ImageCLEF 2009 Medical Task

This paper describes the SINAI team participation in the ImageCLEF 2009 medical task. We explain the experiments accomplished in the medical retrieval task (ImageCLEFmed). We have experimented with query and collection expansion. For expansion, we have carried out experiments using MeSH ontology. With respect to text collection, we have used different collections, one with only caption, and the other with caption and title. Moreover, we have experimented only with textual search, using the LEMUR toolkit as Information Retrieval system.
The all-geodesic furthest neighbor problem for simple polygons

We present an <italic>&Ogr;</italic>(<italic>n</italic> log<italic>n</italic>) time and <italic>&Ogr;</italic> (<italic>n</italic>) space algorithm for the following problem in a simple polygon <italic>P</italic> with <italic>n</italic> vertices: For each vertex <italic>u</italic> of <italic>P</italic>, find another vertex π(<italic>u</italic>) that is furthest from <italic>u</italic>, where the distance between two points is measured by the length of the shortest internal path connecting them in <italic>P</italic>. As a corollary, the longest internal path in <italic>P</italic>, called the geodesic diameter, also can be found within the same time and space bound. All the previously known algorithms for computing the geodesic diameter have required &OHgr;(<italic>n</italic><supscrpt>2</supscrpt>) time in the worst case, e.g. see Chazelle [5], Reif and Storer [16] and Toussaint [19].
FRANQUIA VAREJISTA COMO VANTAGEM COMPETITIVA: MÚLTIPLOS ESTUDOS DE CASO NO SEGMENTO DE FARMÁCIAS E DROGARIAS RETAIL FRANCHISE AS COMPETITIVE ADVANTAGE: MULTIPLE CASE STUDIES IN THE PHARMACY AND DRUGSTORE SEGMENT

Esta pesquisa se insere no campo de estudos sobre varejo. A formacao de franquias tem sido um dos tipos de alianca procurado pelo Varejo Farmaceutico. O objetivo do trabalho foi identificar, dentre as possiveis vantagens competitivas, quais estao motivando a formacao de franquias no segmento de farmacias e drogarias. A pesquisa de campo envolveu a realizacao de entrevistas em profundidade com franqueadores e franqueados de duas redes de franquias do varejo farmaceutico, e sua analise baseou-se na tecnica de analise de conteudo. O artigo apresenta um panorama da competicao no setor de Varejo Farmaceutico brasileiro, um levantamento teorico e multiplos estudos de caso de empresas que passaram pela transformacao de modelo de negocio, transformando-se em um modelo de aliancas sob a forma de franquia. Dentre os achados, observou-se que a estrategia de franquias representou uma oportunidade para o franqueador aumentar rapidamente a sua participacao no mercado varejista brasileiro, assim como para compartilhar custos e riscos, alem de diferenciar-se perante os concorrentes. Por outro lado, identifica-se o risco de perda de competencias, uma vez que as franquias tem um tempo de contrato limitado e durante este periodo o know how sobre o negocio e compartilhado com o franqueado. Do ponto de vista do franqueador evidencia-se que a busca pela franquia tem sido motivada, principalmente, pela possibilidade de aprendizado gerencial sobre um novo negocio.
Glueball spectroscopy on S

For SU(2) gauge theory on the three-sphere we implement the influence of the boundary of the fundamental domain, and in particular the θ-dependence, on a subspace of low-energy modes of the gauge field. We construct a basis of functions that respect these boundary conditions and use these in a variational approximation of the spectrum of the lowest order effective hamiltonian.
Survey of zone tessellation techniques for defined parameters in precision agriculture

Precision agriculture is a trending topic since the last few years. Use of technology and advances in science helps in getting a better yield of crops in a given agricultural area. Novel ground based sensors along with aerial and satellite imagery provide large geo-referenced data sets with high spatial resolution. Management zone delineation is a key task of precision agriculture. From a data-mining point of view this comes down to variant of spatial clustering which has a constraint of keeping the resulting clusters spatially mostly contiguous. Existing spatial clustering algorithms are used to delineate into so-called management zones. However none of the standard clustering algorithms would allow tackling the task on given data set. We discuss a few algorithms for a given data set which returns mostly contiguous tessellation and which can be parameterized easily by human expert. Various attributes obtained by applying different algorithms will be compared. Results on available multi-variate data sets and subsets will be presented. We conclude which is the best algorithm for the given data set.
Control strategy for bi-directional DC/DC converter of a stand-alone wind power system

A novel topology with a bi-directional DC/DC for stand-alone wind power system is adopted, which can maximize the battery life by monitoring and controlling its state of charge(soc) and charge/discharge process. According to the atmospheric conditions, the state of charge of the battery and the load conditions, six possible operation modes of the bi-directional DC/DC are also expounded. This paper proposes a hybrid control strategy with the combination of the PI control and the direct output current control to avoid the voltage surge of the DC bus caused by the mode switching. A 5kW wind power system test bench is developed to verify the effectiveness of the control strategy. The experimental results show that the voltage surge can be limited within 5% and the controller performs well in various wind speed and load conditions.
Scaling-robust fingerprint verification with smartphone camera in real-life scenarios

We propose a new scheme for accurate contactless fingerprint recognition captured with smartphone cameras under various real-life scenarios. The proposed scheme can be structured using three building blocks namely: (1) finger segmentation (2) pre-processing and scaling (3) minutiae extraction and comparison. The proposed finger segmentation scheme is based on Mean Shift Segmentation (MSS) algorithm followed by multiple metrics to accurately segment the finger from the background. We then propose a new scheme to perform the finger scaling to accurately extract the fingerprint region from the segmented finger. Finally, the comparison is carried out based on the minutiae features extracted from the scaled fingerprint images. Extensive experiments are carried out on our recently collected contactless fingerprint dataset consisting of 1800 samples from 25 subjects. In order to effectively evaluate the robustness of the proposed scheme, the whole dataset is constructed using three different smartphone's namely: Nokia N8, iPhone 4 and Samsung S1. The experimental results have shown the effectiveness of the proposed scheme on various complex backgrounds with an Equal Error Rate of 3.74% noted on Samsung S1 smartphone camera.
Comparison of D2 dopamine receptor occupancy after oral administration of quetiapine fumarate immediate-release and extended-release formulations in healthy subjects

Quetiapine is an established drug for treatment of schizophrenia, bipolar disorder, and major depressive disorder. While initially manufactured as an immediate-release (IR) formulation, an extended-release (XR) formulation has recently been introduced. Pharmacokinetic studies show that quetiapine XR provides a lower peak and more stable plasma concentration than the IR formulation. This study investigated if the pharmacokinetic differences translate into different time curves for central D2 dopamine receptor occupancy. Eleven control subjects were examined with positron emission tomography (PET) and the radioligand [11C]raclopride. Eight subjects underwent all of the scheduled PET measurements. After baseline examination, quetiapine XR was administered once-daily for 8 d titrated to 300 mg/d on days 5–8, followed by 300 mg/d quetiapine IR on days 9–12. PET measurements were repeated after the last doses of quetiapine XR and IR at predicted times of peak and trough plasma concentrations. Striatal D2 receptor occupancy was calculated using the simplified reference tissue model. Peak D2 receptor occupancy was significantly higher with quetiapine IR than XR in all subjects (50±4% and 32±11%, respectively), consistent with lower peak plasma concentrations for the XR formulation. Trough D2 receptor occupancy was similarly low for both formulations (IR 7±7%, XR 8±6%). The lower peak receptor occupancy associated with quetiapine XR may explain observed pharmacodynamic differences between the formulations. Assuming that our findings in control subjects are valid for patients with schizophrenia, the study supports the view that quetiapine, like the prototype atypical antipsychotic clozapine, may show antipsychotic effect at lower D2 receptor occupancy than typical antipsychotics.
Few CaV1.3 Channels Regulate the Exocytosis of a Synaptic Vesicle at the Hair Cell Ribbon Synapse

Hearing relies on faithful sound coding at hair cell ribbon synapses, which use Ca2+-triggered glutamate release to signal with submillisecond precision. Here, we investigated stimulus–secretion coupling at mammalian inner hair cell (IHC) synapses to explore the mechanisms underlying this high temporal fidelity. Using nonstationary fluctuation analysis on Ca2+ tail currents, we estimate that IHCs contain ∼1700 Ca2+ channels, mainly of CaV1.3 type. We show by immunohistochemistry that the CaV1.3 Ca2+ channels are localized preferentially at the ribbon-type active zones of IHCs. We argue that each active zone holds ∼80 Ca2+ channels, of which probably <10 open simultaneously during physiological stimulation. We then manipulated the Ca2+ current by primarily changing single-channel current or open-channel number. Effects on exocytosis of the readily releasable vesicle pool (RRP) were monitored by membrane capacitance recordings. Consistent with the high intrinsic Ca2+ cooperativity of exocytosis, RRP exocytosis changed nonlinearly with the Ca2+ current when varying the single-channel current. In contrast, the apparent Ca2+ cooperativity of RRP exocytosis was close to unity when primarily manipulating the number of open channels. Our findings suggest a Ca2+ channel–release site coupling in which few nearby CaV1.3 channels impose high nanodomain [Ca2+] on release sites in IHCs during physiological stimulation. We postulate that the IHC ribbon synapse uses this Ca2+ nanodomain control of exocytosis to signal with high temporal precision already at low sound intensities.
Drug discovery for breast cancer based on big data analytics techniques

Scientific research are nowadays faced to very massive data processing, which consume relatively too much time and effort, that's why researchers have turned to high performance computational (HPC) techniques. In the same context, research on drug discovery has reached a place where it has no choice but using HPC and Big Data Processing Systems to accomplish its objectives in reasonable periods of time, Virtual Screening (VS) is considered as one of the most computationally intensive and heavy process, it plays an important role in designing new drugs and has to be done as fast as possible in order to effectively dock ligands in huge databases to a given protein receptor. On the other hand, breast cancer is one of the most dangerous diseases of world, in the last decade; more than 1.5 million new cases are diagnosed each year, with more than 400 thousands deaths. These statistics give very great importance to drug research for this disease. In this context, and in order to ameliorate the drug designing process for breast cancer, we propose in this work, to use Machine Learning Algorithms that are designed for Big Data analysis on top of MapReduce and Mahout in order to pre-filter the huge set of ligands to effectively do virtual screening for the breast cancer protein receptor.
Deep Gray Matter

The aim of the present thesis was to use an iron sensitive MRI imaging technique (SWI), in the context of the inflammatory, demyelinating and (neuro)degenerative disease multiple sclerosis (MS). For several decades, MRI studies using techniques sensitive to iron, such as SWI but also T2, T2*, R2*, FDRI and post‐mortem studies have been conducted in neurodegenerative disorders such as Alzheimer's disease. However, MS has received much less attention, most likely because many have regarded this disease as a strict white matter (WM) disorder, whereas increased iron levels are usually observed in the gray matter (GM). More recent developments in MS research have led to a shift in the focus of research to also include GM pathology. Throughout the present work, the SWI‐filtered phase technique, which is sensitive to (para‐) magnetic substances, most notably iron, was used as a surrogate in vivo marker of iron deposition. In Chapter 2, we first established a baseline for the behavior of SWI phase in healthy aging. From there on, in Chapter 3, we investigated SWI‐derived phase measures in deep GM structures in different stages of MS, starting in early MS cases (pediatric and clinically isolated syndrome) and later stages (relapsing‐remitting and secondary progressive MS). In Chapter 4, the hallmark pathological finding in MS, white matter signal abnormalities/lesions, are investigated for appearance, prevalence, and diagnostic value using SWI. Hallgren and Sourander published a hallmark histological study in 1958 where they observed that non‐heme iron was elevated in older people; occurring mostly in the putamen, caudate nucleus, globus pallidus, substantia nigra, dentate nucleus, and thalamus, as well as the prefrontal, sensory, cerebellar, and motor cortices. 1 Iron levels of the globus pallidus, thalamus, red nucleus, substantia nigra, and sensory and cerebellar cortices increase rapidly during the first decades of life, but remain relatively constant starting at 30 years, with the exception of the thalamus where iron levels slowly decreases after young adulthood (Chapter 2). In contrast, iron contents of the putamen, caudate nucleus, and motor cortex increase somewhat slower, with maximum levels being reached in older age. Many recent studies have used MRI to investigate the link between healthy aging and brain iron levels. Among the MRI techniques used are SWI, magnetic field correlation, quantitative susceptibility mapping, and T2 and R2 relaxometry. 2‐7 Collectively, their main findings imply that MRI changes suggestive of increased iron increase with age. In order to have a reference framework, it …
Effects of differential mobility on biased diffusion of two species

Using simulations and a simple mean-field theory, we investigate jamming transitions in a two-species lattice gas under non-equilibrium steady-state conditions. The two types of particles diffuse with different mobilities on a square lattice, subject to an excluded volume constraint and biased in opposite directions. Varying filling fraction, differential mobility and drive, we map out the phase diagram, identifying first order and continuous transitions between a free-flowing disordered and a spatially inhomogeneous jammed phase. Ordered structures are observed to drift, with a characteristic velocity, in the direction of the more mobile species.
On Automatic Categorization of Open Source Software

The number of Open Source software systems is increasing at a rapid rate. For example, SourceForge currently has about fifty-five thousand software systems registered, twenty-two thousand of which were added in the past twelve months. With such a large number of software projects, locating the right project for a given purpose can obviously be quite challenging. We propose to use automatic software categorization to address this challenge. At present, we leave open the issue of the nature of the categorization, and explore several known approaches like code clones-based similarity metric, decision trees, and latent semantic analysis. The results from applying each of the approaches gives us some insights into the problem space, and sets some directions for further work.
In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population

Aberrant transcriptional activities have been documented in breast cancers. Studies often find some transcription factors to be inappropriately regulated and enriched in certain pathological states. The promoter regions of most target genes have binding sites for their transcription factors. An ample of evidence supports their combinatorial effect on their shared target gene expressions. Here, we used a new statistic method, bivariate CID, to predict combinatorial interaction activity between ERα and a transcription factor (E2F1or GATA3 or ERRα) in regulating target gene expression via four regulatory mechanisms. We identified gene sets in three signal transduction pathways perturbed in breast tumors: cell cycle, VEGF, and PDGFRB. Bivariate network analysis revealed several target genes previously implicated in tumor angiogenesis are among the predicted shared targets, including VEGFA, PDGFRB. In summary, our analysis suggests the importance for the multivariate space of an inferred ERα transcriptional regulatory network in breast cancer diagnostic and therapeutic development.
Multimodal image fusion for next generation NDE systems

Advantages and limitations associated with each NDE modality raises a tradeoff in which no single modality can be identified for a particular application. The necessary elements for building an intelligent NDE system based on image fusion are introduced. An application is presented considering the fusion of optical and eddy current images. Fusion algorithms are implemented using intensity hue saturation (IHS) transform, discrete wavelet transform and IHS with shift invariant wavelet decomposition. Developed image evaluation measures (quality metrics) are adopted to cross the gap between subjective and objective evaluation, which is essential to automate NDE systems in industrial environments. Introduced quality metric depends on mean square error (RMSE), correlation (CORR), peak signal to noise ratio (PSNR) and mutual information (MI). Analysis of the developed system is presented based on experimental data.
Downlink path-based precoding in FDD massive MIMO systems without CSI feedback

For a massive multiple-input-multiple-output (MIMO) system operated under frequency-division duplex (FDD), downlink training was usually considered impractical due to huge amount of pilot signals and feedback overhead. Although some efforts have been spent to reduce such an overhead in recent works, the reduction is still limited. More recently, some precoding methods that do not require downlink training and channel state information (CSI) feedback have been proposed by recognizing some similarity between uplink and downlink channels. The base station may take advantage of such similarity and acquire partial knowledge of the downlink channels using previous uplink channel estimates. In this paper, we propose to further exploit the diversity of a multipath channel in a downlink precoding method without CSI feedback. Specifically, space-time block code (STBC) is applied to enhance the robustness of downlink transmission based on the partial CSI at the transmitter (CSI-T). Simulation results suggest that the proposed method achieves a competitive performance to other methods with only the partial CSI-T.