Emerging roles of regional journals in the accreditation of knowledge in tropical medicine: Biomédica and Memorias do Instituto Oswaldo Cruz, 2007–2015

In the financial market, many fixed-income analysts focus on trajectories of the hotspots. However, limited attention has been paid to the quantitative and qualitative evolution in a rapidly changing research field of fixed-income securities. This paper identifies intellectual turning points and emerging trends in the fixed-income securities field by using bibliometric methods, visualizing complicated networks including co-keyword networks, journal co-citation networks. Highly cited terms, categories, countries and institutes are detected and discussed. By using all 5915 publications published between 1997 and 2016, recorded in SCI core collection and SSCI in fixed-income securities domain and their references (101911), it can be conducted from this research that 10 top journals in the fixed-income field. The output of this article also shows the pioneer authors, the leading institutes and leading countries and regions, and what’s more, the trend of research in fixed-income securities domain. It could possibly be a valuable source for academics and practitioners working in the field of fixed-income securities.     

A semantic-based knowledge fusion model for solution-oriented information network development: a case study in intrusion detection field

Building information networks using semantic based techniques to avoid tedious work and to achieve high efficiency has been a long-term goal in the information management world. A great volume of research has focused on developing large scale information networks for general domains to pursue the comprehensiveness and integrity of the information. However, constructing customised information networks containing subject-specific knowledge has been neglected. Such research can potentially return high value in terms of both theoretical and practical contribution. In this paper, a new type of network, solution-oriented information network, is coined that includes research problems and proposed techniques as nodes, and the relationship between them. A lightweight Semantic-based Knowledge Fusion Model (SKFM) is proposed leveraging the power of Natural Language Processing (NLP) and Crowdsourcing to construct the proposed information networks using academic papers (knowledge) from Scopus. SKFM relies on NLP in terms of automatic components while Crowdsourcing is initiated when uncertain cases arise. Applying the NLP technique assists to develop a semi-automatic knowledge fusion method for saving effort and time in extracting information from academic papers. Leveraging human power in uncertain cases is to make sure the essential concepts for developing the information networks are extracted reliably and connected correctly. SKFM shows a theoretical contribution in terms of lightweight knowledge extraction and reconstruction framework, as well as practical value by providing solutions proposed in academic papers to address corresponding research issues in subject-specific areas. Experiments have been implemented which have shown promising results. In the research field of intrusion detection, the information of attack types and proposed solutions has been extracted and integrated in a graphic manner with high accuracy and efficiency.     

Charge transport in cellular nanoparticle networks: meandering through nanoscale mazes

The transport of electrons through topologically complex two-dimensional Au nanoparticle networks has been investigated using a combination of low temperature (4.5 K) direct current I(V) measurements and numerical simulations. Intricate, spatially correlated nanostructured networks were formed via spin-casting. The topological complexity of the nanoparticle assemblies produces I(V) curves associated with nonlinearity exponents, zeta approximately 4.0. Simulations based on tunneling transport in sparse and inhomogeneous planar networks are used to elucidate the influence of topology on the value of zeta.     

Collaborative network of firms: antecedents and state-of-the-art properties

The concept of collaborative networks has attracted considerable attention, especially in two knowledge domains: innovation and operation management. Although collaborative networks have evolved into many forms, their central role in triggering debates has not gone unnoticed. In this paper, we aim to contribute to these debates through a bibliometric review of a decade of research on collaborative networks (2002–2015). By using a combination of document co-citation and bibliographic coupling analyses, we unveil the origins and evolution of collaborative networks over time by analysing the references from the most important academic contributions. Our goal is threefold: first, to systematise the state of the art in the Input-Process-Output (IPO) framework; second, to elucidate the antecedents of the research field on collaborative networks (research streams originating from the field, main authors and journals) by organising them in six thematic clusters; and third, to show how the antecedents shaped the IPO framework. While ‘collaborative networks’ used to belong to two distinct domains, the research agendas seem to converge because of the interactions between their most active authors. As such, cross-fertilisation occurs with the International Journal of Production Research, which plays a central role in this process.     

复杂网络理论在离散车间生产过程的应用研究综述

目前复杂网络的理论基础已得到深入研究,但其在生产领域的应用状况和趋势分析尚不明确。为此,提出了一个面向离散制造过程的复杂网络理论应用研究框架;通过分析国内外相关文献资料,围绕与制造过程密切相关的产品族/产品设计、零部件制造、产品装配和效能评价4个方面进行综述,对当前取得的成果进行了总结和分析。并通过与Petri网等复杂系统建模方法比较,发现采用复杂网络理论重新定义当前的生产过程,可为生产研究提供新的视角和思路。最后对未来的研究趋势作了进一步展望。     

高速可见光通信的前沿研究进展

本文立足于通信领域近年来备受关注的研究热点--可见光通信,阐述了其研究背景和基础系统架构,围绕材料器件、高速系统、异构网络、水下可见光通信和机器学习等五个前沿研究方向展开了对可见光通信研究进展的探讨,并概述了现阶段高速可见光通信技术面临的若干挑战。最后展望了可见光通信的前景:在未来万物互联的智能时代,可见光通信将以其高速传输的优势成为通信网络中不可缺少的一部分,与其它通信方式合作互补共同造福人类生活。     

Path entropy method for multiple-source water distribution networks

Over the past decade, one of the key research areas for water distribution networks has been the quantification of network reliability. An interesting approach in this research topic is the use of informational entropy as a surrogate measure for the reliability of water distribution network. Research on water distribution network entropy has progressed to the stage where practical applications are possible, but the actual meaning of the network entropy has never been fully elucidated. Recently, an alternative approach to calculate network entropy was proposed, which was termed the path entropy method (PEM). This alternative method was shown to be useful for the case of single-source water distribution networks. The purpose of this article is to explore the use of the PEM on multiple-source networks. A two-source and two-demand water distribution network is analysed to gain insight into the differences between single-source and multiple-source networks in terms of maximum-entropy flow distribution. This leads to the formal proof of the principle governing maximum-entropy flow ratios in multiple-source water distribution networks.     

Electron transport within the wurtzite and zinc-blende phases of gallium nitride and indium nitride

Wide energy gap semiconductors are broadly recognized as promising materials for novel electronic and opto-electronic device applications. As informed device design requires a firm grasp on the material properties of the underlying electronic materials, the electron transport that occurs within the wide energy gap semiconductors has been the focus of considerable study over the years. In an effort to provide some perspective on this rapidly evolving and burgeoning field of research, we review analyzes of the electron transport within some wide energy gap semiconductors of current interest in this paper. In order to narrow the scope of this review, we will primarily focus on the electron transport that occurs within the wurtzite and zinc-blende phases of gallium nitride and indium nitride in this review, these materials being of great current interest to the wide energy gap semiconductor community; indium nitride, while not a wide energy gap semiconductor in of itself, is included as it is often alloyed with other wide energy gap semiconductors, the resultant alloy often being a wide energy gap semiconductor itself. The electron transport that occurs within zinc-blende gallium arsenide will also be considered, albeit primarily for bench-marking purposes. Most of our discussion will focus on results obtained from our ensemble semi-classical three-valley Monte Carlo simulations of the electron transport within these materials, our results conforming with state-of-the-art wide energy gap semiconductor orthodoxy. A brief tutorial on the Monte Carlo electron transport simulation approach, this approach being used to generate the results presented herein, will also be provided. Steady-state and transient electron transport results are presented. The evolution of the field, a survey of the current literature, and some applications for the results presented herein, will also be featured. We conclude our review by presenting some recent developments on the electron transport within these materials. This review is the latest in a series of reviews that have been published on the electron transport processes that occur within the class of wide energy semiconductor materials. The results and references have been updated to include the latest developments in this rapidly evolving field of study.     

Reliable multicast for the Grid: a case study in experimental computer science

In its simplest form, multicast communication is the process of sending data packets from a source to multiple destinations in the same logical multicast group. IP multicast allows the efficient transport of data through wide-area networks, and its potentially great value for the Grid has been highlighted recently by a number of research groups. In this paper, we focus on the use of IP multicast in Grid applications, which require high-throughput reliable multicast. These include Grid-enabled computational steering and collaborative visualization applications, and wide-area distributed computing. We describe the results of our extensive evaluation studies of state-of-the-art reliable-multicast protocols, which were performed on the UK's high-speed academic networks. Based on these studies, we examine the ability of current reliable multicast technology to meet the Grid's requirements and discuss future directions.     

Interconnected logistic networks and protocols: simulation-based efficiency assessment

Logistic networks intensely use means of transportation and storage facilities to deliver goods. However, these logistic networks are still poorly interconnected and this fragmentation is responsible for a lack of consolidation and thus efficiency. To cope with the seeming contradiction of just-in-time deliveries and challenging emissions targets, a major improvement in supply networks is sought here. This new organisation is based on the universal interconnection of logistics services, namely a Physical Internet where goods travel in modular containers for the sake of interconnection in open networks. If from a logical point of view, merging container flows should improve efficiency, no demonstration of its potential has been carried out prior to the here reported research. To reach this potentiality assessment goal, we model the asynchronous shipment and creation of containers within an interconnected network of services, find the best path routing for each container and minimise the use of transportations means. To carry out the demonstration and assess the associated stakes, we use a set of actual flows from the fast-moving consumer goods sector in France. Various transportation protocols and scenarios are tested, revealing encouraging results for efficiency indicators such as CO₂ emissions, cost, lead time, delivery travel time, and so forth. As this is a first work in the field of flows transportation, the simulation model and experiment exposes many further research avenues.     

A Method for Improving CNN-Based Image Recognition Using DCGAN

Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with CNN for the second time. Use DCGAN to generate samples and training in image recognition model, which based by CNN. This method can enhance the classification model and effectively improve the accuracy of image recognition. In the experiment, we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance. This paper applies image recognition technology to the meteorological field.     

Research collaboration in groups and networks: differences across academic fields

The purpose of this paper is to give a macro-picture of collaboration in research groups and networks across all academic fields in Norwegian research universities, and to examine the relative importance of membership in groups and networks for individual publication output. To our knowledge, this is a new approach, which may provide valuable information on collaborative patterns in a particular national system, but of clear relevance to other national university systems. At the system level, conducting research in groups and networks are equally important, but there are large differences between academic fields. The research group is clearly most important in the field of medicine and health, while undertaking research in an international network is most important in the natural sciences. Membership in a research group and active participation in international networks are likely to enhance publication productivity and the quality of research.     

Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field. Deep Extreme Learning Machine (DELM) framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments. The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions. It leads to the concept of vehicle controller making self-decisions. The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations. This overcomes inadequacy of the current in-vehicle route-finding technology and its support. All the relevant route-related information for the ride will be provided to the user based on its availability. Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained. During investigation, it has been observed that proposed framework has the highest accuracy rate with 70% of training (1435 samples) and 30% of validation (612 samples). Simulation results validate the intelligent prediction of the proposed method with 98.88%, 98.2% accuracy during training and validation respectively.     

Multishell Carrier Transport in Multiwalled Carbon Nanotubes

Understanding carrier transport in carbon nanotubes (CNTs) and their networks is important for harnessing CNTs for device applications. Here, we report multishell carrier transport in individual multiwalled CNTs, and films of randomly dispersed multiwalled CNTs, as a function of electric field and temperature. Electrical measurements and first-principles density functional theory calculations indicate transport across CNT shells. Intershell conduction occurs across an energy barrier range of 60-250 meV in individual CNTs, and ~ 60 meV in CNT networks. In both cases, the conductance behavior can be explained based upon field-enhanced carrier injection and defect-enhanced transport, as described by the Poole-Frenkel model.     

Optimisation of the transmission of disruptions in assembly systems

The dynamic analysis of disruption transmission in networks of closed loops formed by machines and intermediate buffers is of vital importance in most production systems. Nevertheless, little research has been done on optimisation in this field. This study analyses the disruption time transmission in a generic assembly system, which has been modelled as a network of closed loops of machines and intermediate buffers. In addition, this modelling has been used to analyse a real automobile assembly line, taking into account variables that have not previously been considered in the literature, such as working regimes of machines, their cycle times, capacities of the intermediate buffers and their minimum contents. The optimal configuration of the intermediate buffers is analysed. Dynamic outlines of these kinds of assembly systems are proposed in order to maximise the transmission of disruption times and, hence, their availability. For this purpose, an algorithm for analysing and optimising availability in this kind of manufacturing system has also been developed.     

Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis

Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of “steel structures”. The collaboration network of scientists in the field has been analyzed using author affiliations extracted from Scopus between 1970 and 2009. We have studied collaboration distribution networks at the micro-, meso- and macro-levels for the 40 years. We compared and analyzed a number of properties of these networks (i.e., density, centrality measures, the giant component and clustering coefficient) for presenting a longitudinal analysis and statistical validation of the evolutionary dynamics of “steel structures” collaboration networks. At all levels, the scientific collaborations network structures were central considering the closeness centralization while betweenness and degree centralization were much lower. In general networks density, connectedness, centralization and clustering coefficient were highest in marco-level and decreasing as the network size grow to the lowest in micro-level. We also find that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.     

The organization of scientific knowledge: the structural characteristics of keyword networks

The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks.     

新型超导体MgB2及其掺杂研究

MgB2是在2001年新发现的超导材料.它作为一种新型超导体材料备受关注且发展迅速,但其较低的不可逆场和较小的高磁场临界电流密度已成为阻碍其发展的关键.化学掺杂由于具有方便快速、能均匀改性等优点而成为当前提高MgB2超导性能的研究热点.重点评述了有关MgB2的元素或化合物掺杂研究的国内外最新进展,并从基础和应用的角度提出了MgB2超导材料需要深入研究的问题.     

Magnetoelektronik. Magnetoelectronics

The discovery of strong spin‐dependent transport processes in magnetic materials has triggered the advent of a new research field with a strong application potential: magnetoelectronics. This field is concerned with the development of microelectronic devices based on magnetotransport phenomena. Since these phenomena occur predominantly in ultrathin film systems, their technological exploitation poses significant challenges to thin film preparation and requires careful spin engineering. Magnetoresistive read heads for hard disks have already been successfully introduced on the market, and further applications in information technology, for example, nonvolatile magnetic memory banks (MRAM) are coming within reach. The realization of active magnetoelectronic devices, however e.g., a spin transistor, will need further research work.