Weighted Distributions: A Brief Review, Perspective and Characterizations

The weighted distributions are widely used in many fields such as medicine, ecology and reliability, to name a few, for the development of proper statistical models. Weighted distributions are milestone for efficient modeling of statistical data and prediction when the standard distributions are not appropriate. A good deal of studies related to the weight distributions have been published in the literature. In this article, a brief review of these distributions is carried out. Implications of the differing weight models for future research as well as some possible strategies are discussed. Finally, characterizations of these distributions based on a simple relationship between two truncated moments are presented

Text stream to temporal network - A dynamic heartbeat graph to detect emerging events on twitter

© 2018, Springer International Publishing AG, part of Springer Nature. Huge mounds of data are generated every second on the Internet. People around the globe publish and share information related to real-world events they experience every day. This provides a valuable opportunity to analyze the content of this information to detect real-world happenings, however, it is quite challenging task. In this work, we propose a novel graph-based approach named the Dynamic Heartbeat Graph (DHG) that not only detects the events at an early stage, but also suppresses them in the upcoming adjacent data stream in order to highlight new emerging events. This characteristic makes the proposed method interesting and efficient in finding emerging events and related topics. The experiment results on real-world datasets (i.e. FA Cup Final and Super Tuesday 2012) show a considerable improvement in most cases, while time complexity remains very attractive

Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

© 2019 Elsevier Ltd With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques

BER evaluation of post-meter PLC services in CENELEC-C band

Low voltage, in-home power-line communications (PLC) networks allow direct communication between smart meters (SM) and in-home devices (IHD). In order to minimize security issues, in many deployment scenarios transmission takes place only towards the IHD to display consumption data, with no backwards channel. As a result, channel estimation is difficult and it is necessary to use robust transmission techniques to mitigate the effect of the impulsive noise within the PLC channel. Performance of such system must be evaluated by taking into account realistic interference and channel models for a broad range of configurations. In this work we focus on performance in terms of bit error rate (BER) of a narrowband PLC (NB-PLC) operating in the CENELEC-C band (125–140 kHz) taking into account realistic noise models. Our system is based on binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulation

Wall Microstructures of High Aspect Ratio Enabled by Near‐Field Electrospinning

Near-field electrospinning (NFES) holds the potential to develop into a versatile additive nanomanufacturing platform. However, the impact of a variety of processing variables remains unresolved.Herein, the effect of solvents used to prepare suitable solutions for 3D microstructuring by electrospinning is studied. 3D straight walls of stacked fibers are fabricated using a layer-by-layer fiber deposition approach. The effect of the choice of substrate material is also explored. The results show that a high vapor pressure, and a low dielectric constant of the solvent, as well as a high substrate conductivity facilitate improved stacking of fiber layers. Utilizing these conditions, 3D stacked walls of polyethylene oxide are fabricated, and a maximum aspect ratio of 191.7 ± 52.6, while using a chromium/gold substrate and dichloromethane/methanol as the solvent is achieved

Electrospun carbon nanofibre-assisted patterning of metal oxide nanostructures

This work establishes carbon nanofibre-mediated patterning of metal oxide nanostructures, through the combination of electrospinning and vapor-phase transport growth. Electrospinning of a suitable precursor with subsequent carbonization results in the patterning of catalyst gold nanoparticles embedded within carbon nanofibres. During vapor-phase transport growth, these nanofibres allow preferential growth of one-dimensional metal oxide nanostructures, which grow radially outward from the nanofibril axis, yielding a hairy caterpillar-like morphology. The synthesis of metal oxide caterpillars is demonstrated using zinc oxide, indium oxide, and tin oxide. Source and substrate temperatures play the most crucial role in determining the morphology of the metal oxide caterpillars, whereas the distribution of the nanofibres also has a significant impact on the overall morphology. Introducing the current methodology with near-field electrospinning further facilitates user-defined custom patterning of metal oxide caterpillar-like structures

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

The correlation between radiative surface defect states and high color rendering index from ZnO nanotubes

Combined surface, structural and opto-electrical investigations are drawn from the chemically fashioned ZnO nanotubes and its heterostructure with p-GaN film. A strong correlation has been found between the formation of radiative surface defect states in the nanotubes and the pure cool white light possessing averaged eight color rendering index value of 96 with appropriate color temperature. Highly important deep-red color index value has been realized > 95 which has the capability to render and reproduce natural and vivid colors accurately. Diverse types of deep defect states and their relative contribution to the corresponding wavelengths in the broad emission band is suggested

The correlation between radiative surface defect states and high color rendering index from ZnO nanotubes

Combined surface, structural and opto-electrical investigations are drawn from the chemically fashioned ZnO nanotubes and its heterostructure with p-GaN film. A strong correlation has been found between the formation of radiative surface defect states in the nanotubes and the pure cool white light possessing averaged eight color rendering index value of 96 with appropriate color temperature. Highly important deep-red color index value has been realized > 95 which has the capability to render and reproduce natural and vivid colors accurately. Diverse types of deep defect states and their relative contribution to the corresponding wavelengths in the broad emission band is suggested