Authorship, Collaboration, and Funding Trends in Implantology Literature: Analysis of Five Journals From 2005 to 2009

Purpose: To identify the trend of authorship in dental implant by exploring the prevalence of coauthored articles and to investigate the collaboration efforts, trends in funding involved in original articles, and their relationships. Materials: Articles published in the Clinical Oral Implants Research, International Journal of Oral & Maxillofacial Implants, Clinical Implant Dentistry and Related Research, Implant Dentistry, and Journal of Oral Implantology from 2005 to 2009 were reviewed. Nonoriginal articles were excluded. For each included articles, number of authors, collaboration efforts, and extramural funding were recorded. Descriptive and analytical statistics (alpha = 0.05), including logistic regression analysis and chi(2) test, were used. Results: From a total of 2085 articles, 1503 met the inclusion criteria. Publications with 5 or more authors increased over time (P = 0.813). The amount of collaboration among different disciplines, institutions, and countries all increased. The greatest increase of collaboration was seen among institutions (P = 0.09). Non-funding studies decreased over time (P = 0.031). There was a strong association between collaboration and funding for the manuscripts during the years studied (OR, 1.5). Conclusion: The number of authors per articles and collaborative studies increased over time in implant-related journals. Collaborative studies were more likely to be funded. (Implant Dent 2011;20:68-75)

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities ill images. This estimation is crucial for the problem of crowd monitoring. and control. The assessment is carried out oil a set of nearly 300 real images captured from Liverpool Street Train Station. London, UK using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight lille segments. Fourier analysis. and fractal dimensions. The estimations of dowel densities are given in terms of the classification of the input images ill five classes of densities (very low, low. moderate. high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model). Bayesian. and an approach based on fitting functions. The results obtained by these three classifiers. using the four texture measures. allowed the conclusion that, for the problem of crowd density estimation. texture analysis is very effective.

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Estimating crowd density with Minkowski fractal dimension

The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. Fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.

Funding of activities of the Caribbean Council for Science and Technology

Includes bibliography

Report of the Workshop on Investment Funding for Renewable Energy and Energy Efficiency Projects in the Caribbean

Includes bibliography

Reputation, VC funding, and innovation: Evidence from the UK Micro and Nanotechnology sector

Startups’ contributions on economic growth have been widely realized. However, the funding gap is often a problem limiting startups’ development. To some extent, VC can be a means to solve this problem. VC is one of the optimal financial intermediaries for startups. Two streams of VC studies are focused in this dissertation: the criteria used by venture capitalists to evaluate startups and the effect of VC on innovation. First, although many criteria have been analyzed, the empirical assessment of the effect of startup reputation on VC funding has not been investigated. However, reputation is usually positively related with firm performance, which may affect VC funding. By analyzing reputation from the generalized visibility dimension and the generalized favorability dimension using a sample of 200 startups founded from 1995 operating in the UK MNT sector, we show that both the two dimensions of reputation have positive influence on the likelihood of receiving VC funding. We also find that management team heterogeneity positively influence the likelihood of receiving VC funding. Second, studies investigating the effect of venture capital on innovation have frequently resorted to patent data. However, innovation is a process leading from invention to successful commercialization, and while patents capture the upstream side of innovative performance, they poorly describe its downstream one. By reflecting the introduction of new products or services trademarks can complete the picture, but empirical studies on trademarking in startups are rare. Analyzing a sample of 192 startups founded from 1996 operating in the UK MNT sector, we find that VC funding has positive effect on the propensity to register trademarks, as well as on the number and breadth of trademarks.

Simulazioni realistiche di algoritmi di Crowd Steering

La simulazione realistica del movimento di pedoni riveste una notevole importanza nei mondi dell'architettonica e della sicurezza (si pensi ad esempio all'evacuazione di ambienti), nell'industria dell'entertainment e in molti altri ambiti, importanza che è aumentata negli ultimi anni. Obiettivo di questo lavoro è l'analisi di un modello di pedone esistente e l'applicazione ad esso di algoritmi di guida, l'implementazione di un modello più realistico e la realizzazione di simulazioni con particolare attenzione alla scalabilità. Per la simulazione è stato utilizzato il framework Alchemist, sviluppato all'interno del laboratorio di ricerca APICe, realizzando inoltre alcune estensioni che potranno essere inglobate nel pacchetto di distribuzione del sistema stesso. I test effettuati sugli algoritmi presi in esame evidenziano un buon guadagno in termini di tempo in ambienti affollati e il nuovo modello di pedone risulta avere un maggiore realismo rispetto a quello già esistente, oltre a superarne alcuni limiti evidenziati durante i test e ad essere facilmente estensibile.

Source of funding in experimental studies of mobile phone use on health: Update of systematic review

A previous review showed that among 59 studies published in 1995–2005, industry-funded studies were least likely to report effects of controlled exposure to mobile phone radiation on health-related outcomes. We updated literature searches in 2005–2009 and extracted data on funding, conflicts of interest and results. Of 75 additional studies 12% were industry-funded, 44% had public and 19% mixed funding; funding was unclear in 25%. Previous findings were confirmed: industry-sponsored studies were least likely to report results suggesting effects. Interestingly, the proportion of studies indicating effects declined in 1995–2009, regardless of funding source. Source of funding and conflicts of interest are important in this field of research.

Investigating the Effects of Various Crowd Characteristics on the Dynamic Properties of an Occupied Structure

One of the challenges for structural engineers during design is considering how the structure will respond to crowd-induced dynamic loading. It has been shown that human occupants of a structure do not simply add mass to the system when considering the overall dynamic response of the system, but interact with it and may induce changes of the dynamic properties from those of the empty structure. This study presents an investigation into the human-structure interaction based on several crowd characteristics and their effect on the dynamic properties of an empty structure. The dynamic properties including frequency, damping, and mode shapes were estimated for a single test structure by means of experimental modal analysis techniques. The same techniques were utilized to estimate the dynamic properties when the test structure was occupied by a crowd with different combinations of size, posture, and distribution. The goal of this study is to isolate the occupant characteristics in order to determine the significance of each to be considered when designing new structures to avoid crowd serviceability issues. The results are presented and summarized based on the level of influence of each characteristic. The posture that produces the most significant effects based on the scope of this research is standing with bent knees with a maximum decrease in frequency of the first mode of the empty structure by 32 percent atthe highest mass ratio. The associated damping also increased 36 times the damping of the empty structure. In addition to the analysis of the experimental data, finite element models and a two degree-of-freedom model were created. These models were used to gain an understanding of the test structure, model a crowd as an equivalent mass, and also to develop a single degree-of-freedom (SDOF) model to best represent a crowd of occupants based on the experimental results. The SDOF models created had an averagefrequency of 5.0 Hz, within the range presented in existing biomechanics research, and combined SDOF systems of the test structure and crowd were able to reproduce the frequency and damping ratios associated with experimental tests. Results of this study confirmed the existence of human-structure interaction andthe inability to simply model a crowd as only additional mass. The two degree-offreedom model determined was able to predict the change in natural frequency and damping ratio for a structure occupied by multiple group sizes in a single posture. These results and model are the preliminary steps in the development of an appropriate methodfor modeling a crowd in combination with a more complex FE model of the empty structure.