1,721,160 research outputs found
Investigation of Surface Nanostructuring, Mechanical Performance and Deformation Mechanisms of AISI 316L Stainless Steel Treated by Surface Mechanical Impact Treatment
No full textNanostructured materials exhibit superior properties with respect to their bulk counterpart. Recently, a new processing method for surface nanostructuring of metallic materials called surface mechanical impact treatment (SMIT) was developed. In this study, the surface microstructural features due to the refinement process of AISI 316L stainless steel by means of SMIT and subsequent mechanical performance were investigated. The effects of SMIT processing parameters, i.e. ball size and treatment duration, were studied in terms of microstructural evolutions using X-ray diffraction, transmission electron microscopy, optical microscopy, and field emission scanning electron microscopy analyses, and mechanical properties through hardness and tensile tests. A gradient nanostructured surface layer was successfully formed on the surface of the treated samples. The mean grain size was measured to be similar to 20 nm in the topmost surface layer and increased with increasing depth. Microstructural examinations showed that the twins and their intersections (rhombic blocks) formed in the surface layers. It was found that the mechanical performance of the treated samples is effectively enhanced. The surface hardness of the treated samples increased about 3 times while the yield strength of the samples increased with increasing SMIT time and size of the ball up to 2.5 times. The grain refinement mechanisms, mechanical properties, and fracture behavior were subsequently analyzed and discussed
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Towards health-aware food recipe recommendation
Get PDFAbstract
Food recommender systems (FRS) play a crucial role in lifestyle services by assisting users in adopting healthier eating habits. In recent years, numerous FRSs have emerged to predict and guide user choices. However, they face several limitations. Traditional FRS rely heavily on past user ratings, neglecting food ingredients and overlooking changes in user preferences over time. They also fail to consider users’ communities and food clusters, encounter explainability issues, lack visual aspects of food images, and often do not align with users’ health goals. Additionally, they primarily focus on single-user scenarios, neglecting group dynamics and emotional insights, thereby limiting their effectiveness in real world scenarios.
To address these challenges, we developed six food recommender systems (FRS I to FRS VI). Initially, our primary focus was on recommendation accuracy. However, we recognized that non-accuracy-based measures such as explainability and transparency are also crucial in FRS. Consequently, our next step was the development of an FRS that not only prioritizes accuracy but also emphasizes explainability and transparency. Recognizing the significance of promoting healthy lifestyles through our recommendations, we endeavored to propose health-aware FRS. We observed that while advocating for healthier food choices, there could potentially be a decrease in user satisfaction and overall system performance. Hence, we aimed to strike a balance between these factors by introducing a fairness-aware approach to healthy recommendations. Moreover, to accommodate group preferences, we formulated health-aware food recommendations tailored to group users. Finally, we sought to enhance overall performance and user satisfaction by incorporating and identifying users’ emotional aspects. This integration caters to both the varied preferences of users and the needs of those with distinct emotional attitudes. Original papers Rostami, M., Oussalah, M., & Farrahi, V. (2022). A novel time-aware food recommender-system based on deep learning and graph clustering. IEEE Access, 10, 52508–52524. https://doi.org/10.1109/access.2022.3175317 https://doi.org/10.1109/access.2022.3175317 Self-archived version Rostami, M., Muhammad, U., Forouzandeh, S., Berahmand, K., Farrahi, V., & Oussalah, M. (2022). An effective explainable food recommendation using deep image clustering and community detection. Intelligent Systems with Applications, 16, 200157. https://doi.org/10.1016/j.iswa.2022.200157 https://doi.org/10.1016/j.iswa.2022.200157 Self-archived version Rostami, M., Farrahi, V., Ahmadian, S., Mohammad Jafar Jalali, S., & Oussalah, M. (2023). A novel healthy and time-aware food recommender system using attributed community detection. Expert Systems with Applications, 221, 119719. https://doi.org/10.1016/j.eswa.2023.119719 https://doi.org/10.1016/j.eswa.2023.119719 Self-archived version Rostami, M., Aliannejadi, M., & Oussalah, M. (2023). Towards health-aware fairness in food recipe recommendation. Proceedings of the 17th ACM Conference on Recommender Systems, 1184–1189. https://doi.org/10.1145/3604915.3610659 https://doi.org/10.1145/3604915.3610659 Rostami, M., Berahmand, K., Forouzandeh, S., Ahmadian, S., Farrahi, V., & Oussalah, M. (2024). A novel healthy food recommendation to user groups based on a deep social community detection approach. Neurocomputing, 576, 127326. https://doi.org/10.1016/j.neucom.2024.127326 https://doi.org/10.1016/j.neucom.2024.127326 Self-archived version Rostami, M., Vardasbi, A., Aliannejadi, M., & Oussalah, M. (2024). Emotional insights for food recommendations. Lecture Notes in Computer Science, 14609, 238–253. https://doi.org/10.1007/978-3-031-56060-6_16 https://doi.org/10.1007/978-3-031-56060-6_16 Self-archived version Tiivistelmä
Ruokasuositusjärjestelmillä on keskeinen rooli elämäntapaa tukevissa palveluissa, sillä ne auttavat käyttäjiä omaksumaan terveellisempiä ruokailutottumuksia. Viime vuosina on kehitetty lukuisia ruokasuositusjärjestelmiä ennustamaan ja ohjaamaan käyttäjien valintoja, mutta niissä on useita rajoituksia. Perinteiset ruokasuositusjärjestelmät perustuvat vahvasti käyttäjien aiempiin arvioihin, eivätkä ne huomioi ruoka-aineiden ainesosia tai muutoksia käyttäjien mieltymyksissä ajan myötä. Ne eivät myöskään ota huomioon käyttäjän yhteisöä ja ruokaklusteria, selitettävyysongelmia, ruokakuvien visuaalista puolta, ja usein ne eivät vastaa käyttäjien terveystavoitteita. Lisäksi ne keskittyvät ensisijaisesti yksittäiskäyttäjien skenaarioihin sekä laiminlyövät ryhmädynamiikan ja käyttäjien erilaiset tunnetilat, mikä rajoittaa niiden tehokkuutta käyttäjillä. Ratkaistaksemme nämä haasteet kehitimme kuusi ruokasuositusjärjestelmää. Aluksi keskityimme ensisijaisesti suositusten tarkkuuteen. Huomasimme, että ei-tarkkuuteen perustuvat mittarit, kuten selitettävyys ja läpinäkyvyys, ovat myös keskeisiä ruokasuositusjärjestelmissä. Tämän seurauksena kehitimme ruokasuositusjärjestelmän, joka ei ainoastaan priorisoi tarkkuutta, vaan korostaa myös selitettävyyttä ja läpinäkyvyyttä.
Ottaen huomioon terveyssuositusten merkityksen terveellisten elämäntapojen edistämisessä, pyrimme ehdottamaan terveyden huomioon ottavia ruokasuositusjärjestelmiä. Huomasimme, että samalla kun edistämme terveellisempiä ruokavalintoja, käyttäjien tyytyväisyys ja järjestelmän yleinen suorituskyky voivat mahdollisesti heikentyä. Tästä syystä pyrimme löytämään tasapainon näiden tekijöiden välillä esittelemällä oikeudenmukaisuuden huomioon ottavan terveellisen suosituslähestymistavan. Lisäksi ryhmien mieltymysten huomioimiseksi laadimme terveyden huomioon ottavia ruokasuosituksia ryhmäkäyttäjille. Lopuksi pyrimme parantamaan järjestelmän kokonaisvaltaista suorituskykyä ja tyytyväisyyttä sisällyttämällä ja tunnistamalla käyttäjän tunnetiloja. Tämä integraatio kehittää käyttäjien yleisiä mieltymyksiä ja erilaisiin tunnetiloihin pohjautuvia tarpeita. Osajulkaisut Rostami, M., Oussalah, M., & Farrahi, V. (2022). A novel time-aware food recommender-system based on deep learning and graph clustering. IEEE Access, 10, 52508–52524. https://doi.org/10.1109/access.2022.3175317 https://doi.org/10.1109/access.2022.3175317 Rinnakkaistallennettu versio Rostami, M., Muhammad, U., Forouzandeh, S., Berahmand, K., Farrahi, V., & Oussalah, M. (2022). An effective explainable food recommendation using deep image clustering and community detection. Intelligent Systems with Applications, 16, 200157. https://doi.org/10.1016/j.iswa.2022.200157 https://doi.org/10.1016/j.iswa.2022.200157 Rinnakkaistallennettu versio Rostami, M., Farrahi, V., Ahmadian, S., Mohammad Jafar Jalali, S., & Oussalah, M. (2023). A novel healthy and time-aware food recommender system using attributed community detection. Expert Systems with Applications, 221, 119719. https://doi.org/10.1016/j.eswa.2023.119719 https://doi.org/10.1016/j.eswa.2023.119719 Rinnakkaistallennettu versio Rostami, M., Aliannejadi, M., & Oussalah, M. (2023). Towards health-aware fairness in food recipe recommendation. Proceedings of the 17th ACM Conference on Recommender Systems, 1184–1189. https://doi.org/10.1145/3604915.3610659 https://doi.org/10.1145/3604915.3610659 Rostami, M., Berahmand, K., Forouzandeh, S., Ahmadian, S., Farrahi, V., & Oussalah, M. (2024). A novel healthy food recommendation to user groups based on a deep social community detection approach. Neurocomputing, 576, 127326. https://doi.org/10.1016/j.neucom.2024.127326 https://doi.org/10.1016/j.neucom.2024.127326 Rinnakkaistallennettu versio Rostami, M., Vardasbi, A., Aliannejadi, M., & Oussalah, M. (2024). Emotional insights for food recommendations. Lecture Notes in Computer Science, 14609, 238–253. https://doi.org/10.1007/978-3-031-56060-6_16 https://doi.org/10.1007/978-3-031-56060-6_16 Rinnakkaistallennettu versio Academic dissertation to be presented with the assent of the Doctoral Programme Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence in Auditorium IT116, Linnanmaa, on 4 December 2024, at 5 p.m.Abstract
Food recommender systems (FRS) play a crucial role in lifestyle services by assisting users in adopting healthier eating habits. In recent years, numerous FRSs have emerged to predict and guide user choices. However, they face several limitations. Traditional FRS rely heavily on past user ratings, neglecting food ingredients and overlooking changes in user preferences over time. They also fail to consider users’ communities and food clusters, encounter explainability issues, lack visual aspects of food images, and often do not align with users’ health goals. Additionally, they primarily focus on single-user scenarios, neglecting group dynamics and emotional insights, thereby limiting their effectiveness in real world scenarios.
To address these challenges, we developed six food recommender systems (FRS I to FRS VI). Initially, our primary focus was on recommendation accuracy. However, we recognized that non-accuracy-based measures such as explainability and transparency are also crucial in FRS. Consequently, our next step was the development of an FRS that not only prioritizes accuracy but also emphasizes explainability and transparency. Recognizing the significance of promoting healthy lifestyles through our recommendations, we endeavored to propose health-aware FRS. We observed that while advocating for healthier food choices, there could potentially be a decrease in user satisfaction and overall system performance. Hence, we aimed to strike a balance between these factors by introducing a fairness-aware approach to healthy recommendations. Moreover, to accommodate group preferences, we formulated health-aware food recommendations tailored to group users. Finally, we sought to enhance overall performance and user satisfaction by incorporating and identifying users’ emotional aspects. This integration caters to both the varied preferences of users and the needs of those with distinct emotional attitudes.Tiivistelmä
Ruokasuositusjärjestelmillä on keskeinen rooli elämäntapaa tukevissa palveluissa, sillä ne auttavat käyttäjiä omaksumaan terveellisempiä ruokailutottumuksia. Viime vuosina on kehitetty lukuisia ruokasuositusjärjestelmiä ennustamaan ja ohjaamaan käyttäjien valintoja, mutta niissä on useita rajoituksia. Perinteiset ruokasuositusjärjestelmät perustuvat vahvasti käyttäjien aiempiin arvioihin, eivätkä ne huomioi ruoka-aineiden ainesosia tai muutoksia käyttäjien mieltymyksissä ajan myötä. Ne eivät myöskään ota huomioon käyttäjän yhteisöä ja ruokaklusteria, selitettävyysongelmia, ruokakuvien visuaalista puolta, ja usein ne eivät vastaa käyttäjien terveystavoitteita. Lisäksi ne keskittyvät ensisijaisesti yksittäiskäyttäjien skenaarioihin sekä laiminlyövät ryhmädynamiikan ja käyttäjien erilaiset tunnetilat, mikä rajoittaa niiden tehokkuutta käyttäjillä. Ratkaistaksemme nämä haasteet kehitimme kuusi ruokasuositusjärjestelmää. Aluksi keskityimme ensisijaisesti suositusten tarkkuuteen. Huomasimme, että ei-tarkkuuteen perustuvat mittarit, kuten selitettävyys ja läpinäkyvyys, ovat myös keskeisiä ruokasuositusjärjestelmissä. Tämän seurauksena kehitimme ruokasuositusjärjestelmän, joka ei ainoastaan priorisoi tarkkuutta, vaan korostaa myös selitettävyyttä ja läpinäkyvyyttä.
Ottaen huomioon terveyssuositusten merkityksen terveellisten elämäntapojen edistämisessä, pyrimme ehdottamaan terveyden huomioon ottavia ruokasuositusjärjestelmiä. Huomasimme, että samalla kun edistämme terveellisempiä ruokavalintoja, käyttäjien tyytyväisyys ja järjestelmän yleinen suorituskyky voivat mahdollisesti heikentyä. Tästä syystä pyrimme löytämään tasapainon näiden tekijöiden välillä esittelemällä oikeudenmukaisuuden huomioon ottavan terveellisen suosituslähestymistavan. Lisäksi ryhmien mieltymysten huomioimiseksi laadimme terveyden huomioon ottavia ruokasuosituksia ryhmäkäyttäjille. Lopuksi pyrimme parantamaan järjestelmän kokonaisvaltaista suorituskykyä ja tyytyväisyyttä sisällyttämällä ja tunnistamalla käyttäjän tunnetiloja. Tämä integraatio kehittää käyttäjien yleisiä mieltymyksiä ja erilaisiin tunnetiloihin pohjautuvia tarpeita
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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