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    Wyświetlanie 1-9 z 9
    Tytuł:
    Comparison of hay and maize silage intake by lambs
    Porównanie pobierania przez jagnięta siana i kiszonki z kukurydzy
    Autorzy:
    Kirilov, A.
    Krachunov, I.
    Dimitrova, A.
    Tematy:
    hay
    maize silage
    intake
    lamb
    animal feeding
    production cost
    animal production
    animal breeding
    animal breed
    Blackface Pleven breed
    Pokaż więcej
    Wydawca:
    Polskie Towarzystwo Łąkarskie
    Powiązania:
    https://bibliotekanauki.pl/articles/2234802.pdf  Link otwiera się w nowym oknie
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rozpoznawanie wzorców cyfrowych pisma ręcznego z użyciem robota edukacyjnego
    Handwritten digit pattern recognition based on education robot
    Autorzy:
    Dimitrova-Grekow, T.
    Sworowska, A.
    Tematy:
    rozpoznawanie wzorców
    pismo ręczne
    drzewo decyzyjne
    graf Hamiltona
    pattern recognition
    handwritten numbers
    decision tree
    Hamilton graph
    Pokaż więcej
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Powiązania:
    https://bibliotekanauki.pl/articles/155788.pdf  Link otwiera się w nowym oknie
    Opis:
    Niniejsza praca prezentuje zaimplementowanie systemu rozpoznającego ręcznie pisane wzorce cyfrowe z użyciem mobilnego układu edukacyjnego LEGO Mindstorms NXT. Został on wybrany ze względu na prostotę w konstrukcji i równocześnie możliwość złożonego programowania. Zbudowany w ramach projektu robot skanujący znaki pisma ręcznego spełnił założenia początkowe. Wyniki zaimplementowanego algorytmu rozpoznającego również pokryły się z oczekiwaniami - system osiągnął skuteczność na poziomie 100% w warunkach idealnych. We względnie utrudnionych warunkach skuteczność spadła do 91%.
    Pattern recognition can be classified depending on the data source, the way data is read, processed and on the implementation of the recognition itself [9]. This paper presents a method of pattern recognition identifying handwritten Arabic numbers. The data is collected by a Lego Mindstorms NXT 2.0 mobile robot using a color sensor. Usually, the input data are gathered by high-precision equipment [2,5], and or have an additional multi-sensor subsystem [1]. Very successive recognition approaches are based on neural networks [3, 4,6] additional supported by statistic [8]. Unfortunately, all these methods require powerful calculations. The environment data read by such a simple educational robot contains many drawbacks: noises, relative stabile confidence etc. The solution we propose solves to some extent the problem using a minimal hardware equipment (Fig. 4) and undemanding computation effort. The built recognition system is divided into two parts. The first part presents the data set collection - the hand-written digits scanning (Fig.1) and the data initial processing. The second one consists of primary and secondary classification (Figs. 2 and 3). The algorithm is based on the undirected graph model [10]. The results of the conducted experiments are very interesting (Tabs. 1 and 2). This encourages further exploration of implementation of the well-known and new recognition methods on minimal hardware.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie algorytmów przeszukiwania grafów do analizy obrazów medycznych
    Analysis of medical images based on graph search algorithms
    Autorzy:
    Dimitrova-Grekow, T.
    Dąbkowski, A.
    Tematy:
    analiza obrazów medycznych
    algorytmy przeszukiwania grafów
    uczenie maszynowe
    eksploracja danych
    rozpoznawanie choroby
    image analysis
    graph search algorithm
    machine learning
    data mining
    disease recognition
    Pokaż więcej
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Powiązania:
    https://bibliotekanauki.pl/articles/156629.pdf  Link otwiera się w nowym oknie
    Opis:
    W artykule przedstawiono wyniki testów niekonwencjonalnego zastosowania metod do przeszukiwania grafów w celu analizy obrazów powstałych z rezonansu magnetycznego głowy. Zaprezentowano GUI do automatycznej obróbki serii obrazów. Zbudowane klasyfikatory wykazały, że metoda BFS analizy plików DICOM, po odpowiednej selekcji cech, pozwala na 100% rozpoznawanie chorych na wodogłowie i ponad 90% zdrowych, co zachęca do dalszych badań i obserwacji, np. czy osoby sklasyfikowane błędnie jako chorzy, po czasie rzeczywiście nie rozwinęli tej choroby.
    There are many methods for image segmentation [1, 2]: threshold, area, edge and hybrid methods. Area methods indicate groups of similar pixels form local regions [3, 4]. Edge methods detect boundaries between homogeneous segments [5, 6, 7]. In this paper we present the results of tests of unconventional implementation of graph search methods for the analysis of images generated from magnetic resonance imaging [8]. We explored the effectiveness of different approaches for dividing areas within a similar gray scale, using adapted graph search algorithms (DFS, BFS) after appropriate modification (Fig. 1). For this purpose, the Weka package (a tool for pre-processing, classification, regression, clustering and data visualization) was used [9]. A training set was generated after analyzing all the series of images from the database. First, we evaluated models created using certain algorithms and compared their efficacy (Tab. 1). This was followed by a selection of attributes (Tab. 2) and a re-evaluation of the models (Tab. 3). Comparison of the results of both evaluations showed that after selection of the relevant product attributes, you can achieve up to 100% detection of patients with hydrocephalus and over 90% proper recognition of healthy persons. This encourages further research and observation, such as whether persons wrongly classified as sick actually developed the disease in time. We designed a web application for the study, written in Windows Azure, as well as a GUI for automatic processing of a series of images (Fig. 2).
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Low-cost Monitoring System of a Sitting Posture
    Autorzy:
    Sieniawski, M.
    Dimitrova-Grekov, T.
    Klimowicz, A.
    Tematy:
    sitting position monitoring
    RaspberryPi
    graphical user interface
    database
    Pokaż więcej
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Powiązania:
    https://bibliotekanauki.pl/articles/114379.pdf  Link otwiera się w nowym oknie
    Opis:
    The article presents a description of sitting posture monitoring system, built on Raspberry_Pi_3b, Tact Switch buttons, a prototype board and a rubber mat. RaspberryPi connected to the sensors communicated with a personal computer. This allows on the one hand to immediately inform the seated about the appearance of no symmetry of the position on the chair and managing the time of sitting and breaks on the other. The proposed system ensures identification of the degree of incorrect seating during work and gives the opportunity to improve the user's posture, which affects many topics related to health and work efficiency. System possesses a graphical user interface. During system operation, monitoring data is collected and can be used for further research towards an optimal impact on the user to get rid of the faulty sitting habits.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants
    Autorzy:
    Samborska, Izabela A.
    Dimitrova, Stella
    Bielecki, Krzysztof
    Gediga, Krzysztof
    Goltsev, Vasilij
    Kalaji, Hazem M.
    Bąba, Wojciech
    Karmowska, Kamila
    Cetner, Magdalena D.
    Piszcz, Urszula
    Kompała-Bąba, Agnieszka
    Dankov, Kolyo
    Opis:
    In natural conditions, plants growth and development depends on environmental conditions, including the availability of micro- and macroelements in the soil. Nutrient status should thus be examined not by establishing the effects of single nutrient deficiencies on the physiological state of the plant but by combinations of them. Differences in the nutrient content significantly affect the photochemical process of photosynthesis therefore playing a crucial role in plants growth and development. In this work, an attempt was made to find a connection between element content in (i) different soils, (ii) plant leaves, grown on these soils and (iii) changes in selected chlorophyll a fluorescence parameters, in order to find a method for early detection of plant stress resulting from the combination of nutrient status in natural conditions. To achieve this goal, a mathematical procedure was used which combines principal component analysis (a tool for the reduction of data complexity), hierarchical k-means (a classification method) and a machine-learning method-super-organising maps. Differences in the mineral content of soil and plant leaves resulted in functional changes in the photosynthetic machinery that can be measured by chlorophyll a fluorescent signals. Five groups of patterns in the chlorophyll fluorescent parameters were established: the ‘no deficiency’, Fe-specific deficiency, slight, moderate and strong deficiency. Unfavourable development in groups with nutrient deficiency of any kind was reflected by a strong increase in F_{o} and \DeltaV/\Deltat_{0} and decline in \phi_{Po}, \phi_{Eo} \delta_{Ro} and \phi_{Ro}. The strong deficiency group showed the suboptimal development of the photosynthetic machinery, which affects both PSII and PSI. The nutrient-deficient groups also differed in antenna complex organisation. Thus, our work suggests that the chlorophyll fluorescent method combined with machine-learning methods can be highly informative and in some cases, it can replace much more expensive and time-consuming procedures such as chemometric analyses.
    Dostawca treści:
    Repozytorium Uniwersytetu Jagiellońskiego
    Artykuł
    Tytuł:
    Body appreciation around the world : measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age
    Autorzy:
    Vilar, Roosevelt
    Ranjbar, Hamed Abdollahpour
    Irrazabal, Natalia
    Silkane, Vineta
    Meireles, Juliana Fernandes Filgueiras
    Ru, Taotao
    Uyzbayeva, Anar
    Miyairi, Maya
    Togas, Constantinos
    Kukić, Miljana
    Whitebridge, Simon
    Hassan, Mohammad Salah
    Hill, Tetiana
    Chaleeraktrakoon, Trawin
    Vintilă, Mona
    Brytek-Matera, Anna
    Voracek, Martin
    Camilleri, Vittorio Emanuele
    Ali, Khawla F.
    Patwary, Muhammad Mainuddin
    Vally, Zahir
    Coelho, Gabriel Lins de Holanda
    Wong, Kah Yan
    Zawisza, Magdalena
    Martinez, Maria Angeles Gomez
    Tevichapong, Passagorn
    Tran, Ulrich S.
    Vega, Luis Diego
    Alexias, George
    Sundgot-Borgen, Christine
    Todd, Jennifer
    Berbert de Carvalho, Pedro Henrique
    Ahmed, Oli
    Aimé, Annie
    Tylka, Tracy L.
    Czub, Marcin
    Kumar, Sanjay
    Šimunić, Ana
    Handelzalts, Jonathan E.
    Park, Yonguk
    Windhager, Sonja
    Furnham, Adrian
    Mechri, Anwar
    Hawks, Steven R.
    Dimitrova, Donka D.
    Debbabi, Sonia Harzallah
    Jiménez-Borja, Verónica
    Jović, Marko
    Du, Hongfei
    Yeung, Victoria Wai Lan
    Dhakal, Sandesh
    Argyrides, Marios
    Petrova, Nadezhda
    Sawamiya, Yoko
    Adebayo, Sulaiman Olanrewaju
    Tovar-Castro, Juan Camilo
    Burakova, Marina
    Imam, Asma
    Kospakov, Aituar
    Ijabadeniyi, Olasupo Augustine
    Martinez-Banfi, Martha
    Sarfo, Jacob Owusu
    Borja-Alvarez, Teresita
    Villegas, Hyxia
    Ayandele, Olusola
    Fuller-Tyszkiewicz, Matthew
    Jović, Marija
    Ten Hoor, Gill
    Beydağ, Kerime Derya
    Šalov, Anđela
    Król-Zielińska, Magdalena
    Kahle, Lisa-Marie
    Ghisi, Marta
    Birovljević, Gorana
    Geller, Shulamit
    Ghorbani, Alireza
    Jovanović, Veljko
    Lee, Hyejoo J.
    Zeeni, Nadine
    Cazzato, Valentina
    Bratland-Sanda, Solfrid
    Choompunuch, Bovornpot
    Enea, Violeta
    Farbod, Farinaz
    Mebarak, Moisés Roberto
    Sapkota, Saphal
    Karkin, Ayşe Nur
    Alp-Dal, Nursel
    Ng, Siu-Kuen
    Hallit, Souheil
    Selvi, Kerim
    Cosmas, Getrude
    Ballesio, Andrea
    Compte, Emilio J.
    Bender, Sóley Sesselja
    Chambers, Tim
    Grano, Caterina
    Örlygsdóttir, Brynja
    Manjary, Mandar
    Aavik, Toivo
    Andrianto, Sonny
    Holenweger, Geraldine
    Lipowska, Małgorzata
    Mesko, Norbert
    Hekmati, Issa
    Barron, David
    Helmy, Mai
    Jankauskiene, Rasa
    Olapegba, Peter Olamakinde
    Bellard, Ashleigh
    Neto, Félix
    Gradidge, Sarah
    Drysch, Marius
    Dzhambov, Angel M.
    Modrzejewska, Adriana
    Baldó, Lidia Márquez
    Neves, Angela Noguiera
    Dalley, Simon E.
    Poštuvan, Vita
    Slezáčková, Alena
    Kueh, Yee Cheng
    McAnirlin, Olivia
    Massar, Karlijn
    Neto, Joana
    Omar, Salma Samir
    Jiménez-Borja, Micaela
    Malik, Sadia
    Schaefer, Katrin
    Prabhu, Vishnunarayan Girishan
    Aruta, John Jamir Benzon R.
    Corrigan, Jennifer
    Wallner, Christoph
    Di Bernardo, Francesca
    Datu, Jesus Alfonso D.
    Kiropoulos, Litza
    Siddique, Rumana Ferdousi
    Razmus, Magdalena
    Hamdan, Motasem
    Pethö, Tatiana
    Nassani, Mohammad Zakaria
    Bahbouh, Radvan
    Alsalhani, Anas B.
    Farrugia, Lorleen
    Modi, Ritu
    Mulgrew, Kate E.
    Prokop, Pavol
    Kumar, Vipul
    Aspden, Trefor
    Atkin, Stephen
    Dadfar, Mahboubeh
    Khieowan, Nuannut
    Tonini, Fernando
    Hanel, Paul H.P.
    Chaiwutikornwanich, Apitchaya
    Zvaríková, Martina
    Stieger, Stefan
    Vanags, Edmunds
    Chen, Qing-Wei
    Tripathi, Pankaj
    Sharifi, Mehdi
    García, Antonio Alías
    Otterbring, Tobias
    Lombardo, Caterina
    Álvares-Solas, Sara
    Rupar, Mirjana
    Pourmahmoud, Sadaf
    Chobthamkit, Phatthanakit
    Koprivnik, Mirjam
    Kuan, Garry
    Seekis, Veya
    Çakır-Koçak, Yeliz
    Kantanista, Adam
    Tipandjan, Arun
    Singh, Govind
    Vidal-Mollón, Jose
    Maïano, Christophe
    Yau, Eric Kenson
    Trangsrud, Lise Katrine Jepsen
    Jiang, Ding-Yu
    Donofrio, Stacey M.
    Pahl, Sabine
    Sahlan, Reza N.
    Dion, Jacinthe
    Shrivastava, Anita
    Amaral, Ana Carolina Soares
    Cerea, Silvia
    Kohli, Neena
    Matera, Camilla
    Krug, Isabel
    Karakiraz, Ahmet
    Kujan, Omar
    Mills, Jacqueline
    Cowden, Richard G.
    Eskin, Mehmet
    Nerini, Amanda
    Browning, Matthew H.E.M.
    Modrzejewska, Justyna
    De Jesus, Avila Odia S.
    Gyene, Gyöngyvér
    Johnson, Evan M.
    Kasten, Erich
    Czepczor-Bernat, Kamila
    Myers, Taryn A.
    Blackburn, Marie-Ève
    Dixson, Barnaby
    Halbusi, Hussam Al
    Fisher, Maryanne L.
    Ramseyer Winter, Virginia L.
    Frederick, David A.
    Lamba, Nishtha
    Akel, Marwan
    Khatib, Salam
    Oda-Montecinos, Camila
    Camacho, Pablo
    Swami, Viren
    da Silva, Wanderson Roberto
    Hřebíčková, Martina
    Vicente-Arruebarrena, Aitor
    Zanetti, Marcelo Callegari
    Schulte-Mecklenbeck, Michael
    White, Mathew P.
    Namatame, Hikari
    Hina, Farah
    Pietschnig, Jakob
    Junqueira, Alessandra Costa Pereira
    Bozogáňová, Miroslava
    Obeid, Sahar
    Olonisakin, Tosin Tunrayo
    O, Jiaqing
    Lukács, Andrea
    Yamamiya, Yuko
    LeBlanc, Liza April
    Lauri, Mary Anne
    Afhami, Reza
    Baceviciene, Migle
    Knittel, Joshua
    Borowiec, Joanna
    Tudorel, Otilia
    Kimong, Patricia Joseph
    Folwarczny, Michał
    El-Jor, Claire
    Chien, Chin-Lung
    Lipowski, Mariusz
    Laus, Maria Fernanda
    İnce, Başak
    Nithiya, Devi
    Dany, Lionel
    Sigurdsson, Valdimar
    Panasiti, Maria Serena
    Graf, Sylvie
    Özsoy, Emrah
    Fian, Leonie
    Chen, Xin
    Opis:
    The Body Appreciation Scale-2 (BAS-2) is a widely used measure of a core facet of the positive body image construct. However, extant research concerning measurement invariance of the BAS-2 across a large number of nations remains limited. Here, we utilised the Body Image in Nature (BINS) dataset - with data collected between 2020 and 2022 - to assess measurement invariance of the BAS-2 across 65 nations, 40 languages, gender identities, and age groups. Multi-group confirmatory factor analysis indicated that full scalar invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional BAS-2 model has widespread applicability. There were large differences across nations and languages in latent body appreciation, while differences across gender identities and age groups were negligible-to-small. Additionally, greater body appreciation was significantly associated with higher life satisfaction, being single (versus being married or in a committed relationship), and greater rurality (versus urbanicity). Across a subset of nations where nation-level data were available, greater body appreciation was also significantly associated with greater cultural distance from the United States and greater relative income inequality. These findings suggest that the BAS-2 likely captures a near-universal conceptualisation of the body appreciation construct, which should facilitate further cross-cultural research.
    Dostawca treści:
    Repozytorium Uniwersytetu Jagiellońskiego
    Artykuł
      Wyświetlanie 1-9 z 9

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