Sökning: "treatment models"
Visar resultat 1 - 5 av 514 uppsatser innehållade orden treatment models.
1. Wireless Sensor Network for Controlling the Varroasis Spread within Bee colonies across a Geographical Region
Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Background: With the global decline of honey bee populations, safeguarding these vital pollinators has become crucial. Varroa destructor mites are a primary threat, weakening bees and facilitating the spread of diseases, which can decimate colonies and disrupt ecosystems. LÄS MER
2. Robustness Analysis of Perfusion Parameter Calculations
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cancer is one of the most common causes of death worldwide. When given optimal treatment, however, the risk of severe illness may greatly be reduced. Determining optimal treatment in turn requires evaluation of disease progression and response to potential, previous treatment. LÄS MER
3. Evaluering av insatser riktade mot spelmissbruk : En scoping study om spelmissbrukares stöd
Kandidat-uppsats, Malmö universitet/Institutionen för socialt arbete (SA)Sammanfattning : Many times, gaming starts as a hobby but can quickly transform into a costly and destructiveaddiction.The overall aim of this paper is to provide an overview of the research field concerninginterventions for adult individuals with gambling addiction through a literature review by doinga scoping study. LÄS MER
4. Hormonal Influence on the Proliferation Potential of C17.2 Neuronal Progenitor Cells
Kandidat-uppsats, Uppsala universitet/Institutionen för biologisk grundutbildningSammanfattning : The brain is arguably the most complex organ of the body: Controlling muscles, maintaining homeostasis, processing information. It has the longest developmental period of any organ, and hormones are essential during its development. LÄS MER
5. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder
Kandidat-uppsats, Lunds universitet/Fysiska institutionenSammanfattning : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. LÄS MER