124,944 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
Anomalous effect of Li-Al codoping in MgB2: A simple explanation
No full textBy means of first-principles calculations we investigate the possibility that coincorporation of Li and Al in MgB2 could result in an effective "isoelectronic" doping, such as to introduce chemical and structural disorder while leaving unchanged the occupation of the sigma and pi bands. Our results show that the effect on electronic structure of codoping in MgB2 is far from trivial, and shed light on the experimental findings indicating a scarce contribution of Li to the superconducting properties of Mg1-x(AlLi)(x)B-2. The latter result has often been unnecessarily interpreted in terms of the experimental difficulty of actually incorporating Li into the samples
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
High specificity of cphA-encoded metallo-β-lactamase from Aeromonas hydrophila AEO36 for carbapenems and its contribution to β-lactam resistance
No full textThe Aeromonas hydrophila AE036 chromosome contains a cphA gene encoding a metallo-beta-lactamase highly active against carbapenem antibiotics. This enzyme was induced in strain AE036 to the same extent by both benzylpenicillin and imipenem. When the cphA gene was inserted into plasmid pACYC184, used to transform Escherichia coli DH5 alpha, the MICs of imipenem, meropenem, and penem HRE664 for recombinant clone DH5 alpha(pAA20R), expressing the Aeromonas metallo-beta-lactamase, were significantly increased, but those of penicillins and cephalosporins were not. When the metallo-beta-lactamase purified from E. coli DH5 alpha(pAA20R) was assayed with several beta-lactam substrates, it hydrolyzed carbapenems but not penicillins or cephalosporins efficiently. These results demonstrate that this metallo-beta-lactamase possesses an unusual spectrum of activity compared with all the other class B enzymes identified so far, being active on penems and carbapenems only. This enzyme may thus contribute to the development of resistance to penems and carbapenems but not other beta-lactams
The Aeromonas hydrophila cphA gene: molecular heterogeneity among class B metallo-beta-lactamases
No full textAn Aeromonas hydrophila gene, named cphA, coding for a carbapenem-hydrolyzing metallo-beta-lactamase, was cloned in Escherichia coli by screening an Aeromonas genomic library for clones able to grow on imipenem-containing medium. From sequencing data, the cloned cphA gene appeared able to code for a polypeptide of 254 amino acids whose sequence includes a potential N-terminal leader sequence for targeting the protein to the periplasmic space. These data were in agreement with the molecular mass of the original Aeromonas enzyme and of the recombinant enzyme produced in E. coli, evaluated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis of crude beta-lactamase preparations followed by renaturation treatment for proteins separated in the gel and localization of protein bands showing carbapenem-hydrolyzing beta-lactamase activity by a modified iodometric technique. The deduced amino acid sequence of the CphA enzyme showed regions of partial homology with both the beta-lactamase II of Bacillus cereus and the CfiA beta-lactamase of Bacteroides fragilis. Sequence homologies were more pronounced in the regions encompassing the amino acid residues known in the enzyme of B. cereus to function as ligand-binding residues for the metal cofactor. The CphA enzyme, however, appeared to share a lower degree of similarity with the two other enzymes, which, in turn, seemed more closely related to each other. These results, therefore, suggest the existence of at least two molecular subclasses within molecular class B metallo-beta-lactamases
The Aeromonas hydrophyla cphA gene: molecular heterogeneity among class B metallo-β-lactamases
No full textAn Aeromonas hydrophila gene, named cphA, coding for a carbapenem-hydrolyzing metallo-beta-lactamase, was cloned in Escherichia coli by screening an Aeromonas genomic library for clones able to grow on imipenem-containing medium. From sequencing data, the cloned cphA gene appeared able to code for a polypeptide of 254 amino acids whose sequence includes a potential N-terminal leader sequence for targeting the protein to the periplasmic space. These data were in agreement with the molecular mass of the original Aeromonas enzyme and of the recombinant enzyme produced in E. coli, evaluated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis of crude beta-lactamase preparations followed by renaturation treatment for proteins separated in the gel and localization of protein bands showing carbapenem-hydrolyzing beta-lactamase activity by a modified iodometric technique. The deduced amino acid sequence of the CphA enzyme showed regions of partial homology with both the beta-lactamase II of Bacillus cereus and the CfiA beta-lactamase of Bacteroides fragilis. Sequence homologies were more pronounced in the regions encompassing the amino acid residues known in the enzyme of B. cereus to function as ligand-binding residues for the metal cofactor. The CphA enzyme, however, appeared to share a lower degree of similarity with the two other enzymes, which, in turn, seemed more closely related to each other. These results, therefore, suggest the existence of at least two molecular subclasses within molecular class B metallo-beta-lactamases
Criteri dell'Informazione e Selezione dei Modelli in Misurazione Funzionale
No full textThe processes of evaluation of environmental stimuli and decision are common in everyday life and in many social and economic situations. These processes are generally described in scientific literature using multi-attribute choice models. These models assume that evaluation of a stimulus described by several attributes results from a multi-stage process (Anderson, 1981; Lynch, 1985): evaluation of the attributes, integration of the values of the attributes and explicit evaluation of the stimulus. Commonly, in this field, experimental settings require the evaluation of a set of stimuli built combining some attributes. A subject evaluator examines the attributes of each stimulus; using her “mental” model of choice (Oral & Kettani, 1989), it assigns a value to attributes and formulate an overall judgment. Finally, subject expresses his opinion in terms of order-ranking, pairwise preference comparisons, values in a rating scale, and so on.
This so-called multi-attribute evaluation suffers of a fundamental difficulty to measure the values of each attribute of a stimulus starting by the overall evaluation of each subject. Basically, the problem is to derive each value decomposing the overall judgment (i.e. the response output). This difficulty in measuring is typical in most of the often complementary multi-attribute models traditions, as those of Conjoint Analysis (Luce & Tukey, 1964; Krantz & Tversky, 1971; Green & Rao, 1971) or Information Integration Theory (IIT: Anderson, 1970, 1981, 1982).
According to Anderson’s IIT, cognitive system give a subjective value to each characteristic of a stimulus, and the values are put together in a overall judgment using a specific integration function. IIT describe integration modalities using different mathematical rules, and functional measurement is the methodology proposed to determine and measure the integration function. Functional measurement use factorial experiments, selecting some attributes of a stimulus and combining them in factorial plans. Usually, subject’s evaluations for each cell of experimental design are reported on a category scale, and each subject replicates each evaluation for more trials. Starting from subject’s evaluations, functional measurement aims to quantify the value of each level of factors and its importance in the global judgment, for each subject evaluator or group of subjects.
Anderson’s theory suggests that the most widely used integration rules are of three fundamental and simple kinds: additive, multiplicative and weighted average. Statistical techniques as the analysis of variance can be used to detect the integration rule on the basis of the goodness of fit.
The averaging rule in particular can account for interaction effects between factors, splitting evaluation in two components: scale value and weight, which can be identified and measured separately (Zalinski & Anderson, 1989). If scale value represents the location of the level of attribute on the response scale, the weight represents his importance into global judgment. Averaging model provides a useful way to manage interaction between factors, surpassing the assumption of independence on which most applications of multi-attribute choice models are based. However, the model presents some critical points about the estimation issue, and for this motivation it potential is not fully exploited up until now. In this research work, a new method for parameter estimation for averaging model is proposed. The method provides a way to select the best set of parameters to fit data, and aims to overcome some problems that have limited the use of the model.
According to this new method, named R-Average (Vidotto & Vicentini, 2007; Vidotto, Massidda & Noventa, 2010), the choice of optimal model is made according to so-called “principle of parsimony”: the best model is the “simplest” one which found the best compromise between explanation of phenomenon (explained variance) and structural complexity (number of different weight parameters). Selection process use in combination two goodness-of-fit indexes: Akaike Information Criterion (AIC; Akaike, 1974) and Bayesian Information Criterion (BIC; Schwarz, 1978). Both indexes are derived starting from the logarithm of the residual variance weighted for the number of observations, and by penalizing the models with additional parameters. AIC and BIC differ in penalty function - since the BIC imposes a larger penalty for complex models than the AIC does - and are very useful for model comparison.
In this research work, two version of R-Average method are presented. This two versions are one evolution of the other, and both methods are structured in some procedures to perform estimation. Basically, R-Average consists of three procedures: EAM Procedure, DAM Procedure and Information Criteria (IC) Procedure. EAM, DAM and IC differ in constraints imposed on weights during the optimization process.
EAM Procedure constrains all the weight within each factor to be equal, estimating an Equal-weight Averaging Model. This model is the optimum in terms of parsimony, because it presents the smallest number of parameters (one single weight for all levels of each factor). In fact, it is defined as “parsimonious” a simple model, in which the weights are equal. Differently, DAM Procedure does not impose constraints on the weights, leaving their free to vary. Thus, this procedure may converge to a complete Differential-weight Averaging Model, which is the less parsimonious model (i.e. all the weights of each level of each factor are different).
The core of R-Average method is the Information Criteria Procedure. This procedure is based on idea that, from a psychological point of view, a simple model is more plausible than a complex model. For this reason, estimation algorithm is not oriented to search parameters that explain the greater proportion of variance, but search a compromise between explained variance and model complexity. A complex model will be evaluated as better than a simpler one only if the allows a significantly higher degree of explanation of phenomenon.
IC Procedure search the model, trying to keep (in the “forward” version) or to make (in the “backward” version) all the weights equal. In the forward version, the procedure starts from the EAM model and spans all the possible combination of weights, modifying it: initially one by one, then two by two, then three by three and so on. For each combination, the procedure tries to diversifies weights. From time to time, using BIC and AIC indexes, the procedure selects the best set of parameters and assume the selected model as reference for the following step (if an evidence of improvement is found). In the backward version, the procedure starts from the DAM model and spans all the possible combinations of weights, trying to equalize them. BIC and AIC are used to compare the new models with the reference model: if a new model is detected as better than the reference one, it will used as new reference for following steps.
Finally, all the estimated models by the procedures are compared, and the best model based on information criteria is preferred.
The original formulation of the averaging model was modified in the evolution of the basic R-Average method. This reformulation considers the weight not as simply w parameters but as w = exp(t). This exponential transformation leads to a solution for classical problem of uniqueness which affect averaging formulation (Vidotto, 2011). Furthermore, this reformulation justifies the application of cluster analysis algorithms on weight values, necessary for the clustering procedure of experimental subjects on the basis of their similarity. In fact, the distance between two t values can be evaluated in terms of simply difference. Differently, the distance between two w values can be evaluated only in terms of ratio between them. This allows to use clustering algorithms of subjects based on matrices of proximity between parameters.
The performance of R-Average was tested using Monte Carlo studies and practical applications in three different research fields: in marketing, in economic decision theory and in interpersonal trust.
Results of Monte Carlo studies show a good capability of the method to identify parameters of averaging model. Scale parameters are in general well estimated. Differently, weight estimation is a bit more critical. Punctual estimation of the real value of weights are not precise as the estimation of scale values, in particular as the standard deviation of the error component in observed data increases. However, estimations appears reliable, and equalities between weights are identified. The increasing of the number of experimental trials can help model selection when the errors present a greater standard deviation. In summary, R-Average appear as an useful instrument to select the best model within the family of averaging models, allowing to manage particular multi-attribute conditions in functional measurement experiments.
R-Average method was applied in a first study in marketing field. In buying a product, people express a preference for particular products: understanding cognitive processes underlying the formulation of consumers’ preferences is an important issue. The study was conducted in collaboration with a local pasta manufacturer, the Sgambaro company. The aims of research were three: understand the consumer’s judgment formulation about a market product, test the R-Average method in real conditions, and provide to Sgambaro company useful information for a good marketing of its product. Two factors was manipulated: the packaging of the Sgambaro’s pasta (Box with window, Box without window and Plastic bag) and the price (0.89€, 0.99€, 1.09€).
Analyses started considering evaluations of the product express by participants: for each subject, parameters of averaging model was estimated. Since the consumers population is presumably not homogeneous in preferences, the overall sample has been split in three clusters (simply named A, B and C) by an cluster analysis algorithm. For both Price and Packaging factors, different clusters showed different ratings. Cluster A express judgments that are positioned on the center of scale, suggesting as participants are not particularly attracted by this products. By contrast, Cluster B express positive judgments, and Cluster C express globally negative with the exception of the package “box with window”. For packaging, it observes that the box with window, although is not the preferred one in the three clusters, has always positive evaluations, while judgments on other packaging are inconsistent across groups. Therefore, if the target of potential consumers for the product is the general population, the box with window can be considered the most appreciated packaging.
Moreover, in Cluster C ANOVA shows a significant interaction between Price and Packaging. In fact, estimated parameters of averaging model show that Cluster C is greatly affected by a high price. In this cluster the highest price had a double weight in the final ratings, therefore the positive influence on the judgment of the “box with window” packaging could be invalidated. It’s important to notice that the group which is more sensitive to the high price is also that one which gave the lowest ratings compared to the other clusters.
In a second experiment, the R-Average method has been applied in a study in the field of economic decision marking under risk. The assumption that moved the study is that, when a person must evaluate an economic bet in a risky situation, person integrates cognitively the economic value of bet and the probability to win. In the past, Shanteau (1974) shown that integration between value and probability is made according a multiplicative rule. The study, as Lynch (1979), highlighted that when the situation concern two simultaneous bets, each one composed from a value and a probability, judgments for double bet is different to the sum of judgments for single bets. This observation, named subadditivity effect, violate the assumptions of Expected Utility Theory.
The proposed study analyze the convenience/satisfaction associated with single and duplex bets. The study proposed to participants two kind of bets. A first group of bets involved a good (Mobile Phones), and the other one, a service (free SMS per day); to each good/service was associated the a probability to obtained him. Two experimental conditions was defined. In the first condition, subjects judge bets considering that phones come from a good company, and SMS service came from a untrustworthy provider. In the reverse condition, subjects judge bets considering that phones was made with low-quality and come from a untrustworthy company, and SMS service come from a strong and trustworthy provider.
For duplex bets, the presence of averaging integration model was hypnotized, and the parameters of model was estimated using R-Average on each subject. Results show that the integration in presence of a duplex bet is fully compatible with an averaging model: the averaging and not adding appear the correct integration rule.
In the last experiment, averaging model and R-Average methodology were applied to study trust beliefs in three contexts of everyday life: interpersonal, institutional and organizational. Trusting beliefs are a solid persuasion that trustee has favorable attributes to induce trusting intentions. Trusting beliefs are relevant factors in making an individual to consider another individual as trustworthy. They modulate the extent to which a trustor feels confident in believing that a trustee is trustworthy. According to McKnight, Cummings & Chervany (1998), the most cited trusting beliefs are: benevolence, competence, honesty/integrity and predictability.
The basic idea under the proposed study is that beliefs might be cognitive integrated in the concept of trustworthiness with some weighting processes. The R-Average method was used to identify parameters of averaging model for each participant. As main result, analysis shown that, according to McKnight, Cummings & Chervany (1998), the four main beliefs play a fundamental role in judging trust. Moreover, agreeing with information integration theory and functional measurement, an averaging model seems to explain individual responses. The great majority of participants could be referred to the differential-weight case. While scale values show a neat linear trend with higher slopes for honesty and competence, weights show differences with higher mean values, still, for honesty and competence. These results are coherent with the idea that different attributes play a different role in the final judgment: indeed, honesty and competence seem to play the major role while predictability seems less relevant. Another interesting conclusion refers to the high weight of the low level of honesty; it seems to show how a belief related to low integrity play the most important role for a final negative judgment. Finally, the different tilt of the trend for the levels of the attributes in the three situational contexts suggests a prominent role of the honesty in the interpersonal scenarios and of the competence in the institutional scenarios.
In conclusion, information integration theory and functional measurement seem to represent an interesting approach to comprehend the human judgment formulation. This research work proposes a new method to estimate parameters of averaging models. The method shows a good capability to identify parameters and opens new scenarios in information integration theory, providing a good instrument to understand more in detail the averaging integration of attributesI processi di valutazione degli stimoli ambientali e di decisione sono comuni nella vita quotidiana e in tante situazioni di carattere sociale ed economico. Questi processi sono generalmente descritti dalla letteratura scientifica utilizzando modelli di scelta multi-attributo. Tali modelli assumono che la valutazione di uno stimolo descritto da più attributi sia il risultato di un processo a più stadi (Anderson, 1981; Lynch, 1985): valutazione degli attributi, integrazione dei valori e valutazione esplicita dello stimolo. Comunemente, in questo campo, le situazioni sperimentali richiedono la valutazione di un set di stimoli costruiti combinando diversi attributi. Un soggetto valutatore esamina gli attributi di ogni stimolo; usando il solo modello “mentale” di scelta (Oral e Kettani, 1989), assegna un valore agli attributi e formula un giudizio globale. Infine, il soggetto esprime la sua opinione in termini di ordinamento, preferenze a coppie, valori su una scala numerica e così via.
Questa cosiddetta valutazione multi-attributo soffre di una fondamentale difficoltà nel misurare i valori di ogni attributo di uno stimolo partendo dalle valutazioni complessive di ogni soggetto. Fondamentalmente, il problema è derivare ogni valore decomponendo il giudizio complessivo (cioè la risposta in output). Questa difficoltà di misurazione è tipica di molte delle spesso complementari tradizioni dei modelli multi-attributo, come la Conjoint Analysis (Luce e Tukey, 1964; Krantz e Tversky, 1971; Green e Rao, 1971) o la Teoria dell’Integrazione delle Informazioni (IIT: Anderson, 1970, 1981, 1982).
Secondo la IIT di Anderson, il sistema cognitivo fornisce un valore soggettivo a ogni caratteristica di uno stimolo, e tali valori vengono combinati in un giudizio complessivo utilizzando una specifica funzione d’integrazione. La IIT descrive le modalità d’integrazione utilizzando differenti regole matematiche, e la misurazione funzionale è la metodologia proposta per determinare e misurare la funzione d’integrazione. La misurazione funzionale si serve di esperimenti fattoriali, selezionando alcuni attributi di uno stimolo e combinandoli in piani fattoriali. Solitamente, le valutazioni dei soggetti per ogni cella del disegno sperimentale sono riportate su una category scale, e ogni soggetto ripete la valutazione per più prove. Partendo dalle valutazioni soggettive, la misurazione funzionale mira a quantificare il valore di ogni livello dei fattori e la sua importanza nel giudizio complessivo, per ogni soggetto valutatore o gruppo di soggetti.
La teoria di Anderson suggerisce che le regole d’integrazione più ampiamente utilizzate sono di tre fondamentali e semplici tipologie: additiva, moltiplicativa e di media ponderata (averaging). Tecniche statistiche come l’analisi della varianza possono essere utilizzare per individuare la regola d’integrazione sulla base della bontà dell’adattamento.
La regola averaging in particolare è in grado di tenere in considerazione gli effetti d’interazione tra i fattori, scindendo la valutazione in due componenti: valore di scala e peso, che possono essere identificati e misurati separatamente (Zalisnki e Anderson, 1989). Se il valore di scala rappresenta il posizionamento del livello del fattore sulla scala di risposta, il peso rappresenta la sua importanza nel giudizio complessivo. Il modello averaging fornisce una via molto utile per gestire gli effetti d’interazione tra i fattori, superando l’assunto d’indipendenza sul quale molte applicazioni dei modelli di scelta multi-attributo sono basate. Tuttavia, il modello presenta alcuni punti critici relativi alla questione della stima, e per questo motivo il suo potenziale non è stato pienamente sfruttano fin’ora. In questo lavoro di ricerca viene proposto un nuovo metodo per la stima dei parametri del modello averaging. Il metodo consente di selezionare il miglior set di parametri per adattare i dati, e mira a superare alcuni problemi che ne hanno limitato l’uso.
Secondo questo nuovo metodo, chiamato R-Average (Vidotto e Vicentini, 2007; Vidotto, Massidda e Noventa, 2010), la scelta del miglior modello è fatta in accordo al cosiddetto “principio di parsimonia”: il miglior modello è quello più “semplice”, che trova il miglior compromesso tra spiegazione del fenomeno (varianza spiegata) e complessità strutturale (numero di parametri di peso diversi). Il processo di selezione usa in combinazione due indici di bontà dell’adattamento: l’Akaike Information Criterion (AIC; Akaike, 1974) e il Bayesian Information Criterion (BIC; Schwartz, 1978). Entrambi gli indici sono ricavati partendo dal logaritmo della varianza residua pesata per il numero di osservazioni, e penalizzando i modelli con parametri aggiuntivi. AIC e BIC differiscono nella funzione di penalizzazione – dato che il BIC impone una penalità maggiore ai modelli con più parametri – e sono molto utili per la comparazione fra modelli.
In questo lavoro di ricerca vengono presentate due versioni del metodo R-Average. Queste due versioni sono una l’evoluzione dell’altra, ed entrambi i metodi sono strutturati in diverse procedure per eseguire la stima. Fondamentalmente, R-Average consta di tre procedure: procedura EAM, procedura DAM e procedura Information Criteria (IC). EAM, DAM e IC differiscono nei vincoli imposti sui pesi durante il processo di ottimizzazione.
La procedura EAM vincola tutti i pesi all’interno di ogni fattore a essere uguali, stimando un modello a pesi uguali. Questo modello è il migliore in termini di parsimonia, perché presenta il minor numero di parametri (uno unico per ogni fattore). Infatti, si definisce come “parsimonioso” un modello semplice, nel quale i pesi sono uguali. Diversamente, la procedura DAM non impone alcun vincolo sui pesi, lasciandoli liberi di variare. Così, questa procedura può potenzialmente convergere verso un modello averaging a pesi completamente diversi (dove cioè tutti i pesi dei livelli di ogni fattore sono dive
Influence of Mg deficiency on crystal structure and superconducting properties in MgB2 single crystals
No full textThe effects of high-temperature vacuum-annealing-induced Mg deficiency in MgB(2) single crystals grown under high pressure were investigated. As the annealing temperature was increased from 800 to 975 degrees C, the average Mg content in the MgB(2) crystals systematically decreased while T(c) remains essentially unchanged and the superconducting transition slightly broadens from similar to 0.55 to similar to 1.3 K. The reduction in the superconducting volume fraction was noticeable already after annealing at 875 degrees C. Samples annealed at 975 degrees C are partially decomposed and the Mg site occupancy is decreased to 0.92 from 0.98 in as-grown crystals. Annealing at 1000 C completely destroys superconductivity. X-ray diffraction analysis revealed that the main final product of decomposition is polycrystalline MgB(4) and thus the decomposition reaction of MgB(2) can be described as 2MgB(2)(s) --> MgB(4)(s) + Mg(g). First-principles calculations of the Mg(1-x)(V(Mg))(x)B(2) electronic structure, within the supercell approach, show a small downshift of the Fermi level. Holes induced by the vacancies go to both sigma and pi bands. These small modifications are not expected to influence T(c), in agreement with observations. The significant reduction in the superconducting volume fraction without noticeable T(c) reduction indicates the coexistence, within the same crystal, of superconductive and nonsuperconductive electronic phases, associated with regions poor and rich in Mg vacancies
- …
