Iskanje po katalogu digitalne knjižnice

Opis: Background: Studies on food craving and consumption often overlook the interconnectedness of risk factors, assuming uniform mechanisms that drive individuals to (over)consume food. This project seeks to address this gap by leveraging a precision health framework to explore whether multimodal clustering can predict weight and eating outcomes after six months, providing a more nuanced understanding of individual variability. Methods: The project will include a longitudinal study, encompassing several sub-studies where self-report, electrophysiological, and time series dynamic data will be collected at three time points. At baseline, participants will complete comprehensive assessments, including an electroencephalography (EEG) experiment and a one-week experience sampling study (ESM). Machine learning techniques will be employed to uncover distinct participant clusters, characterized by unique patterns of food consumption and weight changes over six months. Markers that best differentiate these profiles will be identified with explainable AI techniques, which aim to make machine learning model outputs understandable by highlighting the key features or patterns driving predictions, enabling personalized insights into key factors contributing to eating behaviors and weight management. Discussion: By exploring the variability of mechanisms influencing food consumption, eating regulation, and weight gain, we aim to uncover subgroups of individuals who are most affected by specific influences, such as stress, emotion regulation difficulties, or sleep deprivation. This project will advance theoretical understanding by integrating multimodal data and emphasizing idiographic methods to capture individual variability. Findings will provide a foundation for future research on precision approaches to eating behaviors and may offer insights into personalized strategies for prevention and management of both normative and disordered eating patterns.
Ključne besede: food cue reactivity, EEG, experienxe sampling methodology, personalized medicine, achine learning, explainable artificial inteligence
Objavljeno v DKUM: 19.12.2025; Ogledov: 0; Prenosov: 0
Celotno besedilo (1,60 MB)
Gradivo ima več datotek! Več...

Opis: The environment hosts a diversity of microorganisms whose potential for biotechnological applications has not yet been exhausted. The quest of our study was to find isolates of Pichia kudriavzevii from the environment that could be used as new biotechnological agents. Moreover, we aimed to explore the resource efficiency for microbial cultivation, in particular the efficiency of spent coffee grounds (SCG), an easily accessible waste coffee product with a high unutilized organic content. In this study, Pichia kudriavzevii strain ZMUM_K002, a yeast strain isolated from a grape pomace compost, was investigated. Antifungal susceptibility, particularly fluconazole susceptibility, was assessed, and the strain’s biotechnological potential by comparing its ability to utilize low-cost carbon sources, including SCG, with a natural isolate of Saccharomyces cerevisiae (strain ZMUM_K003) was assessed. The P. kudriavzevii strain ZMUM_K002 exhibited higher fluconazole susceptibility and yielded more than 30% more biomass in optimized media formulations compared to S. cerevisiae ZMUM_K003. These findings demonstrate that P. kudriavzevii ZMUM_K002 has the potential for efficient biomass production in sustainable industrial biotechnology, particularly in processes requiring high biomass yields on alternative substrates.
Ključne besede: Pichia kudriavzevii, Candida krusei, environmental sampling, biomass production, sauerkraut, safety assessment, spent coffee grounds
Objavljeno v DKUM: 21.03.2025; Ogledov: 0; Prenosov: 8
Celotno besedilo (1,93 MB)
Gradivo ima več datotek! Več...

Opis: Optimizing cutting parameters in fiber laser cutting of austenitic stainless steel is challenging due to the complex interplay of multiple variables and quality metrics. To solve this problem, Latin hypercube sampling was used to ensure a comprehensive and efficient exploration of the parameter space with a smaller number of trials (185), coupled with feedforward neural networks for predictive modeling. The networks were trained with a leave-oneout cross-validation strategy to mitigate overfitting. Different configurations of hidden layers, neurons, and training functions were used. The approach was focused on minimizing dross and roughness on both the top and bottom areas of the cut surfaces. During the testing phase, an average MSE of 0.063 and an average MAPE of 4.68% were achieved by the models. Additionally, an experimental test was performed on the best parameter settings predicted by the models. Initial modelling was conducted for each quality metric individually, resulting in an average percentage difference of 1.37% between predicted and actual results. Grid search was also per formed to determine an optimal input parameter set for all outputs, with predictions achieving an average ac curacy of 98.34%. Experimental validation confirmed the accuracy and robustness of the model predictions, demonstrating the effectiveness of the methodology in optimizing multiple parameters of complex laser cutting processes.
Ključne besede: laser cutting optimization, cut surface quality, dross formation, Latin hypercube sampling, feedforward neural network
Objavljeno v DKUM: 10.01.2025; Ogledov: 0; Prenosov: 28
Celotno besedilo (3,38 MB)
Gradivo ima več datotek! Več...

Ključne besede: self-sampling, Pap smear, screening
Objavljeno v DKUM: 23.01.2023; Ogledov: 549; Prenosov: 56
Celotno besedilo (101,64 KB)
Gradivo ima več datotek! Več...
Gradivo je zbirka in zajema 1 gradivo!

Ključne besede: statistics, statistical methods, commercial business, economy, probability, distributions, sampling, analysis of variance, experimental design, regression analysis, forecasting, analysis
Objavljeno v DKUM: 06.06.2012; Ogledov: 1654; Prenosov: 42
Povezava na celotno besedilo