| 1. |
- Andersson, Martin, et al.
(författare)
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Editorial: developments at the Annals of regional science 2020–2021
- 2022
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Ingår i: Annals of Regional Science. - : Springer Science and Business Media LLC. - 0570-1864 .- 1432-0592. ; 68:1
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The editors-in-chief of the Annals of Regional Science offer an overview and analysis of recent developments at the journal from January 2020 through December 2021, a time period hampered by the COVID-19 pandemic. Annal’s Impact Factor increased substantially to 2.646 in 2020. Moreover, submissions increased from pre-COVID times. A new development is the shifting of source regions for articles accepted for publication. For the first time, China tied with the USA to lead the distribution of acceptances by country. Special Issues continue to be important components of the journal.
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| 2. |
- Erlandsson, Fredrik, 1981-, et al.
(författare)
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Crawling Online Social Networks
- 2015
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Ingår i: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015). - : IEEE Computer Society. ; , s. 9-16
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Konferensbidrag (refereegranskat)abstract
- Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.
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| 3. |
- Erlandsson, Fredrik, 1981-
(författare)
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Human Interactions on Online Social Media : Collecting and Analyzing Social Interaction Networks
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis. Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis. With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.
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| 4. |
- Kim, Brian H. S., et al.
(författare)
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Reflecting on a dynamic biennium : The Annals of Regional Science 2022–2023
- 2024
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Ingår i: The annals of regional science. - : Springer Science+Business Media B.V.. - 0570-1864 .- 1432-0592. ; 72:3, s. 683-690
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The 2024 editorial update of The Annals of Regional Science reaffirms the journal’s dedication to transparency and scholarly excellence in the field of regional science. Despite the challenges posed by the pandemic, the journal has demonstrated resilience and continued to serve as a pivotal platform for scholarly discourse. This update highlights the journal’s recent milestones, including changes to the editorial board with new members bringing fresh perspectives to drive the journal’s mission forward. The editorial discusses the journal’s performance, encompassing its impact factor and the diverse range of topics covered, emphasizing its role in advancing research aligned with Sustainable Development Goals, particularly SDG 8. The ARSC continues to evolve, embracing changes that reflect current trends and maintaining its influence in the academic community. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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| 5. |
- Larsson, Martin, et al.
(författare)
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An Initial Study on Applying Active Noise Control to an Insulated Box Fan Used in Ventilation System Applications
- 2009
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Konferensbidrag (refereegranskat)abstract
- In many different applications and buildings fans are used to remove unwanted and used air. These fans often generate broadband and tonal noise. Commonly, passive resistive silencers are used to attenuate noise generated by different types of fans installed in ventilation systems. Passive silencers tend to become bulky and impractical when designed for low frequency attenuation. However, active noise control (ANC) is a technique known for its ability to produce high levels of attenuation in the low frequency range, even with a relatively moderate sized ANC system. This paper presents an initial study performed to investigate the possibilities of applying ANC to a radial fan installed inside a box, an insulated box fan. The box is connected to a duct system and can for example be used as a waste air fan. The primary interest in this application, when the fan is used as a waste air fan, is to attenuate the noise generated on the suction side, since that side generates noise into a particular room. Investigations were carried out to determine where the ANC system should be installed, e.g. inside the box, in the duct connected to the box etc. Factors considered were for example, turbulence, standing waves, the type of noise generated by the fan (tonal, broadband, or a combination), and space limitations. The noise generated by the fan was found to be dominated by a tonal component, but also to have broadband energy in the low frequency range. Further, a feedforward ANC system was applied on the suction side, producing approximately 28 dB attenuation of the tonal component, and 5-10 dB attenuation of the broadband noise between 50 and 200 Hz.
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| 6. |
- Mhathesh, T. S. R., et al.
(författare)
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A 3d convolutional neural network for bacterial image classification
- 2021
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Ingår i: Advances in Intelligent Systems and Computing. - Singapore : Springer. - 9789811552847 ; , s. 419-431
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Konferensbidrag (refereegranskat)abstract
- Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.
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