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    Wyświetlanie 1-3 z 3
    Tytuł:
    Mouldability and its Recovery Properties of 2D Plain Woven Para-Aramid Fabric for Soft Body Armour Applications
    Formowalność i odprężenie elastyczne tkaniny p-aramidowej 2D stosowanej w miękkich pancerzach
    Autorzy:
    Abtew, Mulat A.
    Loghin, Carment
    Cristian, Irina
    Boussu, François
    Bruniaux, Pascal
    Chen, Yan
    Wang, Lichuan
    Powiązania:
    https://bibliotekanauki.pl/articles/233652.pdf
    Data publikacji:
    2019
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    mouldability
    moulding recovery
    woven para-aramid fabric
    fabric layers
    soft body armour
    formowalność
    odzyskiwanie form
    tkanina para-aramidowa
    warstwy tkaniny
    pancerz
    Opis:
    Mouldability, along with other mechanical properties, is a very crucial material parameter in various technical textile applications, from composites to soft body armour products. Moreover, the mouldability and recovery behaviours of the material will be affected by various internal and external paramters before, during and after the forming process. The current research particularly tried to study the effects of blank-holder pressure (BHP) and the number of layers not only on the moulding characterstics but also on the recovery behaviour of plain woven p-aramid fabrics made from a high-performance yarn with a linear density of 930 dTex. Samples with various numbers of layers were arranged in the same orientation for the moulding process. The moulding approach utilised a specific moulding device in a low-speed forming process and a predefined semi-hemispherically shaped punch for all specimens.Various important dry textile material moulding characteristics and, most importantly, the moulding recovery properties, such as warp and weft direction drawing-in recovery, center high-point recovery, shear angle recovery etc. were investigated.
    Formowalność wraz z innymi właściwościami mechanicznymi jest bardzo ważnym parametrem materiałowym w różnych technicznych zastosowaniach tekstylnych, od kompozytów po miękkie pancerze. Ponadto na formowalność i odprężenie elastyczne materiału mają wpływ różne parametry wewnętrzne i zewnętrzne przed, podczas i po procesie formowania. W pracy szczególnie starano się zbadać wpływ nacisku ślepej próby (BHP) i liczby warstw nie tylko na charakterystykę formowania, ale także na odprężenie elastyczne tkanin p-aramidowych wykonanych z wysokowydajnej przędzy o gęstości liniowej 930 dTex. Próbki o różnej liczbie warstw ułożono w tej samej orientacji do procesu formowania. Zbadano cechy formowania materiału tekstylnego oraz, co najważniejsze, odprężenie elastyczne zarówno w kierunku osnowy i wątku.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2019, 6 (138); 54-62
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Big data management algorithms in artificial Internet of Things-based fintech
    Autorzy:
    Andronie, Mihai
    Iatagan, Mariana
    Uță, Cristian
    Hurloiu, Iulian
    Dijmărescu, Adrian
    Dijmărescu, Irina
    Powiązania:
    https://bibliotekanauki.pl/articles/19902795.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    big data management algorithms
    artificial intelligence
    Internet of Things
    fintech
    banking
    capital markets
    Opis:
    Research background: Fintech companies should optimize banking sector performance in assisting enterprise financing as a result of firm digitalization. Artificial IoT-based fintech-based digital transformation can relevantly reverse credit resource misdistribution brought about by corrupt relationship chains. Purpose of the article: We aim to show that fintech can decrease transaction expenses and consolidates firm stock liquidity, enabling excess leverage decrease and cutting down information asymmetry and transaction expenses across capital markets. AI- and IoT-based fintechs enable immersive and collaborative financial transactions, purchases, and investments in relation to payment tokens and metaverse wallets, managing financial data, infrastructure, and value exchange across shared interactive virtual 3D and simulated digital environments. Methods: AMSTAR is a comprehensive critical measurement tool harnessed in systematic review methodological quality evaluation, DistillerSR is harnessed in producing accurate and transparent evidence-based research through literature review stage automation, MMAT appraises and describes study checklist across systematic mixed studies reviews in terms of content validity and methodological quality predictors, Rayyan is a responsive and intuitive knowledge synthesis tool and cloud-based architecture for article inclusion and exclusion suggestions, and ROBIS appraises systematic review bias risk in relation to relevance and concerns. As a reporting quality assessment tool, the PRISMA checklist and flow diagram, generated by a Shiny App, was used. As bibliometric visualization and construction tools for large datasets and networks, Dimensions and VOSviewer were leveraged. Search terms were “fintech” + “artificial intelligence”, “big data management algorithms”, and “Internet of Things”, search period was June 2023, published research inspected was 2023, and selected sources were 35 out of 188. Findings & value added: The growing volume of financial products and optimized operational performance of financial industries generated by fintech can provide firms with multifarious financing options quickly. Big data-driven fintech innovations are pivotal in banking and capital markets in relation to financial institution operational efficiency. Through data-driven technological and process innovation capabilities, AI system-based businesses can further automated services.
    Źródło:
    Oeconomia Copernicana; 2023, 14, 3; 769-793
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
    Autorzy:
    Lăzăroiu, George
    Gedeon, Tom
    Rogalska, Elżbieta
    Andronie, Mihai
    Frajtova Michalikova, Katarina
    Musova, Zdenka
    Iatagan, Mariana
    Uță, Cristian
    Michalkova, Lucia
    Kovacova, Maria
    Ștefănescu, Roxana
    Hurloiu, Iulian
    Zabojnik, Stanislav
    Stefko, Robert
    Dijmărescu, Adrian
    Dijmărescu, Irina
    Geamănu, Marinela
    Powiązania:
    https://bibliotekanauki.pl/articles/39832736.pdf
    Data publikacji:
    2024
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    deep and machine learning
    COVID 19
    prediction
    detection
    diagnosis
    organizational management
    hospital
    Opis:
    Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.
    Źródło:
    Oeconomia Copernicana; 2024, 15, 1; 27-58
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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