Scientific co-operation in Europe and the citation of multinationally authored papers

Under the sponsorship of the U.S. National Science Foundation, CHI Research, Inc. developed the bibliometric indicators for the U.S. National Science Board'sScience Indicators Reports starting withScience Indicators 1972. In the work reported here, for the Commission of the European Communities, CHI has extended the Science Indicators techniques and database to a study of publication, coauthorship and citation within 28 scientific fields related to various European Community programs.Perhaps the most important finding of the research was that internationally coauthored papers — papers authored by scientists affiliated with institutions in more than one EC country — were cited two times as highly as papers authored by scientists working at a single institution within a single country. These EC-EC internationally coauthored papers were cited as highly as EC-Non EC and Non-EC papers. This indicates that the internationally linked European science is of as high impact as any other science in the world.A second key finding was that, after compensating for national scientific size, the degree of international coauthorship did not appear to be particularly dependent upon size. However, linguistic and cultural factors were found to be very strong. The patterns of coauthorship amongst the European countries are far from homogeneous, and are quite heavily affected by linguistic, historical, and cultural factors.Finally, it was found that international coauthorship is increasing steadily, both within and outside of the Community, with some evidence that international cooperation is increasing more rapidly in scientific fields that have been targeted by the Commission.This work has been sponsored by the MONITOR programme of the Commission of the European Communities.     

Effect of the use of mineral filler on the properties of concrete

Engineers of the concrete technology are increasingly concerned with the material passing through a sieve of the size under 0.149 mm. Materials called very fine aggregate or mineral filler may affect the performance of concrete in an either positive or a negative way. Discussions on aggregate containing very fine material are vitally important. Washing the aggregate residue has been the sole way to solve this matter to date. This is mainly based on the debatable opinion that materials of this kind are regarded as clay material. The goal of the study was to determine how the content of mineral filler might affect properties of concrete. Two types of aggregates with different amounts of cement and mineral filler were used. Basically, mineral filler replaced sand. The effect of applying different amounts of mineral filler on concrete was then determined. The addition of 7-10% of mineral filler to fine aggregate (0-2 mm) was found to considerably improve the properties of concrete.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

Abstract: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.     

Decades of progress, or the progress of decades?

In the almost 40?years since we wrote Evaluative bibliometrics enormous advances have been made in data availability and analytic technique. The journal impact factor of the 1960s has clearly not kept up with the state of the art. However, for both old and new indicators, basic validity and relevance issues remain, such as by what standard can we validate our results, and what external use can appropriately be made of them? As funding support becomes more difficult, we should not lose sight of the necessity to again demonstrate the importance of our research, and must keep in mind that it is the relevance of our results that count, not the elegance of our mathematics.     

The influence of operating and device parameters on the maximum efficiency and the maximum output power of thermoelectric generator

Thermoelectric power generators are one of the promising green energy sources. The operating and the generator parameters influence the generator output performance. In the present study, the influence of the operating and the generator parameters on the maximum output power and the efficiency of the thermoelectric power generator are examined. The output power corresponding to the maximum efficiency and the maximum attainable output power of the generator are compared. It is found that the maximum power of the thermoelectric generator corresponding to the high Figure of Merit is very sensitive to the operating temperature. The maximum power attainable is larger than that its counterpart corresponding to the maximum generator efficiency. Copyright © 2010 John Wiley & Sons, Ltd.