Iskanje po katalogu digitalne knjižnice

Opis: Diplomsko delo se osredotoča na odnos prebivalcev Republike Slovenije do uporabe sončne energije v gospodinjstvih s pomočjo fotovoltaičnih – sončnih elektrarn. Preučili smo teoretično podlago področja, opravljene raziskave s tega področja ter mnenja uporabnikov spletnih forumov na temo domačih fotovoltaičnih elektrarn. Na tej podlagi smo sestavili anketni vprašalnik in s pomočjo metode vzorčenja s snežno kepo preko elektronske pošte ter z objavo na relevantnih spletnih forumih pridobili mnenja prebivalstva na obravnavano temo. Rezultate anketne raziskave smo analizirali, opravili diskusijo na obravnavano temo in podali priporočila.
Ključne besede: sonce, fotovoltaične elektrarne, gospodinjstvo, net metering, baterije
Objavljeno v DKUM: 23.12.2025; Ogledov: 0; Prenosov: 0
Celotno besedilo (447,97 KB)

Opis: This study investigated the impact of information systems (IS) success on Project Success (PS) within hybrid and remote work environments, drawing empirical evidence from the world's largest consulting firm. By integrating the DeLone and McLean IS success model with established PS frameworks, the research examined how IS components—system quality, user satisfaction, and use/ intent to use—influence key PS dimensions: project management success, impact on the team, impact on the customer, and project investment success. Data from 198 employees were analyzed using structural equation modeling, confirming all hypothesized relationships. Results revealed that IS net benefits significantly enhance PS across all dimensions, while system quality and user satisfaction further drive IS effectiveness. The study underscores the critical role of robust digital infrastructure in optimizing project outcomes, offering theoretical insights and practical recommendations for organizations navigating hybrid work models. Limitations and future research directions are also discussed.
Ključne besede: hybrid work environment, DeLone and McLean model, structural equation modeling, remote work, digital transformation, system quality, user satisfaction, net benefits, project management
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (828,79 KB)
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Opis: Diplomsko delo obravnava načrtovanje in razvoj mobilne aplikacije za sestavljanje osebnih programov telesne vadbe. Na osnovi analize strokovne literature in obstoječih aplikacij smo opredelili ključne parametre, ki so potrebni za ustrezno personalizacijo telesne vadbe, ter potrebne funkcionalnosti, kot so upoštevanje razpoložljive opreme, počutja in morebitnih poškodb, animiran prikaz izvajanja vaj in prikaz aktiviranih mišičnih skupin posameznega programa in vaje. Aplikacija vključuje tudi podporo na osnovi umetne inteligence za odgovore na morebitna vprašanja uporabnika. Razvoj je potekal v .NET MAUI z uporabo MySQL baze.
Ključne besede: telesna vadba, mobilna aplikacija, umetna inteligenca, .NET MAUI
Objavljeno v DKUM: 04.09.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (2,60 MB)

Opis: Cross-platform mobile application development has gained significant traction in recent years, driven by the growing demand for efficient, cost-effective solutions that cater to both iOS and Android platforms. This paper presents a state-of-the-art review of crossplatform mobile application development, emphasizing the industry trends, framework popularity, and adoption in the job market. By analyzing developer preferences, community engagement, and market demand, this study provides a comprehensive overview of how cross-platform mobile development frameworks shape the mobile development landscape. The research employs a data-driven methodology, drawing insights from three key categories: Developer Sentiment and Survey Data, Community Engagement and Usage Data, and Market Adoption and Job Market Data. By analyzing these factors, the study identifies the key challenges and emerging trends shaping cross-platform mobile application development. It assesses the most widely used frameworks, comparing their strengths and weaknesses in real-world applications. Furthermore, the research examines the industry adoption patterns and the presence of these frameworks in job market trends. Unlike earlier research, which included now-obsolete platforms like Windows Phone and frameworks such as Xamarin, this study is tailored to the current cross-platform mobile application development market landscape. The conclusions offer actionable insights for developers and researchers, equipping them with the knowledge needed to navigate the evolving cross-platform mobile application development ecosystem effectively.
Ključne besede: cross-platform mobile application development, flutter, react native, .NET MAUI, mobile app engineering, framework evolution, performance optimization, developer experience, job market trends
Objavljeno v DKUM: 29.05.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (1,80 MB)

Opis: Accurate automated localization of cephalometric landmarks in skull X-ray images is the basis for planning orthodontic treatments, predicting skull growth, or diagnosing face discrepancies. Such diagnoses require as many landmarks as possible to be detected on cephalograms. Today’s best methods are adapted to detect just 19 landmarks accurately in images varying not too much. This paper describes the development of the SCN-EXT convolutional neural network (CNN), which is designed to localize 72 landmarks in strongly varying images. The proposed method is based on the SpatialConfiguration-Net network, which is upgraded by adding replications of the simpler local appearance and spatial configuration components. The CNN capacity can be increased without increasing the number of free parameters simultaneously by such modification of an architecture. The successfulness of our approach was confirmed experimentally on two datasets. The SCN-EXT method was, with respect to its effectiveness, around 4% behind the state-of-the-art on the small ISBI database with 250 testing images and 19 cephalometric landmarks. On the other hand, our method surpassed the state-of-the-art on the demanding AUDAX database with 4695 highly variable testing images and 72 landmarks statistically significantly by around 3%. Increasing the CNN capacity as proposed is especially important for a small learning set and limited computer resources. Our algorithm is already utilized in orthodontic clinical practice.
Ključne besede: detection of cephalometric landmarks, skull X-ray images, convolutional neural networks, deep learning, SpatialConfiguration-Net architecture, AUDAX database
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 11
Celotno besedilo (2,46 MB)
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