125,945 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Optimized uncertainty estimation in the calibration of two port networks transmission coefficient
No full textThe uncertainty evaluation in the calibration of two-port
networks transmission coefficient is here dealt with. Attention is
focused on the covariance contribution of the uncertainty expression
and on the effect of mismatch terms. To this aim, the approach
suggested by the Guide for the expression of uncertainty in
measurement (GUM) is discussed, and solutions are proposed
aimed at optimally combining all the uncertainty terms to be
considered. To confer reliability and generality to the research
activity, a number of experiments are conducted through a proper
test bench. The ultimate goal is to provide practical hints for
improving the calibration accuracy of two-port networks
transmission coefficient, and simplifying the derivation of the
standard uncertainty
Group-wise functional community detection through joint Laplacian diagonalization
No full textView at Publisher| Export | Download | Add to List | More... Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 8674 LNCS, Issue PART 2, 2014, Pages 708-715 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014; Boston, MA; United States; 14 September 2014 through 18 September 2014; Code 107426 Group-wise functional community detection through joint Laplacian diagonalization (Conference Paper) Dodero, L.a, Gozzi, A.b, Liska, A.b, Murino, V.a, Sona, D.a a Pattern Analysis and Computer Vision (PAVIS), Istituto Italiano di Tecnologia, Genova, Italy b Center for Neuroscience and Cognitive Systems at UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy View references (20) Abstract There is a growing conviction that the understanding of the brain function can come through a deeper knowledge of the network connectivity between different brain areas. Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is becoming one of the most important imaging modality widely used to understand network functionality. However, due to the variability at subject scale, mapping common networks across individuals is by now a real challenge. In this work we present a novel approach to group-wise community detection, i.e. identification of functional coherent sub-graphs across multiple subjects. This approach is based on a joint diagonalization of two or more graph Laplacians, aiming at finding a common eigenspace across individuals, over which clustering in fewer dimension can then be applied. This allows to identify common sub-networks across different graphs. We applied our method to rs-fMRI dataset of mouse brain finding most important sub-networks recently described in literature
Crop row detection through RPAS surveys to optimise on-farm irrigation management
No full textClimate change and competition among water users are increasingly leading to a reduction of water availability for irrigation; at the same time, traditionally non-irrigated crops require irrigation to achieve high quality standards. In the context of precision agriculture, particular attention is given to the optimization of on-farm irrigation management, based on the knowledge of within-field variability of crop and soil properties, to increase crop yield quality and ensure an efficient water use. Unmanned Aerial Vehicle (UAV) imagery is used in precision agriculture to monitor crop variability, but in the case of row-crops, image post-processing is required to separate crop rows from soil background and weeds. This study focuses on the crop row detection and extraction from images acquired through a UAV during the cropping season of 2018. Thresholding algorithms, classification algorithms, and Bayesian segmentation are tested and compared on three different crop types, namely grapevine, pear, and tomato, for analyzing the suitability of these methods with respect to the characteristics of each crop. The obtained results are promising, with overall accuracy greater than 90% and producer's accuracy over 85% for the class "crop canopy". The methods' performances vary according to the crop types, input data, and parameters used. Some important outcomes can be pointed out from our study: NIR information does not give any particular added value, and RGB sensors should be preferred to identify crop rows; the presence of shadows in the inter-row distances may affect crop detection on vineyards. Finally, the best methodologies to be adopted for practical applications are discussed
Integrating Geophysical and Multispectral Data to Delineate Homogeneous Management Zones within a Vineyard in Northern Italy
No full textSoil electrical conductivity (EC) maps obtained through proximal soil sensing (i.e., geophysical data) are usually considered to delineate homogeneous site-specific management zones (SSMZ), used in Precision Agriculture to improve crop production. The recent literature recommends the integration of geophysical soil monitoring data with crop information acquired through multispectral (VIS-NIR) imagery. In non-flat areas, where topography can influence the soil water conditions and consequently the crop water status and the crop yield, considering topography data together with soil and crop data may improve the SSMZ delineation. The objective of this study was the fusion of EC and VIS-NIR data to delineate SSMZs in a rain-fed vineyard located in Northern Italy (Franciacorta), and the assessment of the obtained SSMZ map through the comparison with data acquired by a thermal infrared (TIR) survey carried out during a hot and dry period of the 2017 agricultural season. Data integration is performed by applying multivariate statistical methods (i.e., Principal Component Analysis). The results show that the combined use of soil, topography and crop information improves the SSMZ delineation. Indeed, the correspondence between the SSMZ map and the CWSI map derived from TIR imagery was enhanced by including the NDVI information
A model for the operations to render epidemic-free a hog farm infected by the Aujeszky disease
No full textWe present here a case study for modelling the control of the Aujeszky disease, in a farm declared virus-free. The model is
validated on the available data. Simulations are performed to assess different containment strategies for the epidemic. Final
recommendations indicate that a strict reduction of biohazards in the farrowing unit should be enforced. Also neglecting the
third inoculation in the vaccination protocol leads to a sensible and quantifiable increase of the prevalence of the disease.
The findings indicate that it is more important to keep biosafety at a high level in the farrowing unit rather than strive for the
highest standards in the gestation unit. Also the importance of a properly implemented vaccination appears fundamental,
and its absence can be quantified via our simulation
Pragmatic Case Studies as a Source of Unity in Applied Psychology
No full textTo unify or not to unify applied psychology: that is the question. In this article we review pendulum swings in the historical efforts to answer this question—from a comprehensive, positivist, “top-down,” deductive yes between the 1930s and the early 60s, to a postmodern no since then. A rationale and proposal for a limited, “bottom-up,” inductive yes in applied psychology is then presented, employing a case-based paradigm that integrates both positivist and postmodern themes and components. This paradigm is labeled “pragmatic psychology” and, its specific use of case studies, the “Pragmatic Case Study Method” (“PCS Method”). We call for the creation of peer-reviewed journal-databases of pragmatic case studies as a foundational source of unifying applied knowledge in our discipline. As one example, the potential of the PCS Method for unifying different angles of theoretical regard is illustrated in an area of applied psychology, psychotherapy, via the case of Mrs. B. The article then turns to the broader historical and epistemological arguments for the unifying nature of the PCS Method in both applied and basic psychology.Peer reviewe
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