Dor pélvica crônica (DPC) afeta grande númerode mulheres e seu manejo ainda permanece complexo e insatisfatório. Estudos têm demonstrado um envolvimento do fator de crescimento neuronal (FCN) no processo de cronificação da dor. Participação hormonal neste processo também tem sido aventada, visto autores terem demonstrado influência estro/progestacional sobre nociceptores tanto direta quanto indiretamente através da influência exercida sobre os fatores neurotróficos. Foi objetivo deste estudo, verificar a associação entre a expressão do fator de crescimento neuronal (FCN), seu receptor (trk A) e os receptores de estrogênio e progesterona no peritôneo pélvico com a presença de dor pélvica crônica. Para tal foi realizado um estudo transversal incluindo um grupo de 22 mulheres com DPC, 8 com DPC e usuárias de anticoncepcional oral (DPC/ACO) e 7 sem dor. A dor foi analisada pela escala analógica visual (EAV) e questionário de McGill. Foi realizado imunohistoquímica para avaliar FCN e seu receptor trk A, receptores de estrogênio (RE) e progesterona (RP). A expressão de FCN teve media de 5, variando de 0 a 8, no grupo DPC, 5,5 no grupo DPC/ACO variando 3 a 8, e no grupo sem dor de 5 variando de 3 a 8 (p>0,05). A expressão de trk A apresentou media de 6, variandode 3 a 8, no grupo DPC, 6 no grupo DPC/ACO, variando de 4 a 8, e 6 no grupo sem dor variando de 4 a 6 (p>0,05). A expressão do RE apresentou média 4 no grupo DPC, variando de 0 a 8, 3,5 no grupo DPC/ACO variando de 0 a 8, e 7 no grupo sem dor, variando de 6 a 8 (p<0,05). A expressão do RP teve média 6,5 no grupo DPC, variando de 0 a 8, 5 no grupo DPC/ACO, variando de 0 a 7, e 7 no grupo sem dor, variando de 5 a 8 (p>0,05). Nossos resultados sugerem um papel anti-nociceptivo do estrogênio no peritôneo pélvico de mulheres no menacme, não mediado por expressão de FCN ou trk A. / Chronic pelvic pain (CPP) affects a great number of women and its management still remains complex and unsatisfactory. Studies have shown an involvement of the neuronal growing factor (NGF) in the process of permanence of pain. Hormonal participation in this process has also been put forward, as some authors have demonstrated estro/progestational influence under nociceptors direct or indirectly through their influence on neurotrofic factors. This study aimed to verify the association among the expression of neuronal growing factor (NGF), its receptor (TrKA) and the estrogen and progesterone receptors in the pelvic peritoneum with the presence of chronic pelvic pain. A transversal study was carried out including a group of 22 women with CPP, 8 with CPP and users of oral anticonceptional (CPP/OAC) and 7 without pain. The pain was analized by the visual analogic scale (VAS) and McGill\'s questionnaire. Imunehistochemical was performed to evaluate the NGF and its receptor TrKA, estrogen (ER) and progesteron (PR) receptors. The expression of NGF was an average of 5, varying from 0 to 8, in group CPP, 5,5 in group CPP/ OAC varying from 3 to 8, and in the group without pain varying from 3 to 8 (p>0,05). The expression of TrKA presented an average of 6, varying from 3 to 8, in the group CPP, 6 in the group CPP/OAC, varying from 4 to 8, and 6 in the group without pain varying from 4 to 6 (p>0,05). The expression of ER presented an average of 4 in the group CPP, varying from 0 to 8, 3,5 in group CPP/OAC varying from 0 to 8, and 7 in group without pain, varying from 6 to 8 (p<0,05). The expression of PR had an average 6,5 in the group CPP, varying from 0 to 8,5 in the group CPP/OAC, varying from 0 to 7, and 7 in the group without pain, varying from 5 to 8 (p>0,05). Our studies suggest an anti- nociceptive rule of estrogen in the pelvic peritoneum of women in menacme, not mediated by expression of NGF or TrKA.

Dor pélvica crônica (DPC) afeta grande númerode mulheres e seu manejo ainda permanece complexo e insatisfatório. Estudos têm demonstrado um envolvimento do fator de crescimento neuronal (FCN) no processo de cronificação da dor. Participação hormonal neste processo também tem sido aventada, visto autores terem demonstrado influência estro/progestacional sobre nociceptores tanto direta quanto indiretamente através da influência exercida sobre os fatores neurotróficos. Foi objetivo deste estudo, verificar a associação entre a expressão do fator de crescimento neuronal (FCN), seu receptor (trk A) e os receptores de estrogênio e progesterona no peritôneo pélvico com a presença de dor pélvica crônica. Para tal foi realizado um estudo transversal incluindo um grupo de 22 mulheres com DPC, 8 com DPC e usuárias de anticoncepcional oral (DPC/ACO) e 7 sem dor. A dor foi analisada pela escala analógica visual (EAV) e questionário de McGill. Foi realizado imunohistoquímica para avaliar FCN e seu receptor trk A, receptores de estrogênio (RE) e progesterona (RP). A expressão de FCN teve media de 5, variando de 0 a 8, no grupo DPC, 5,5 no grupo DPC/ACO variando 3 a 8, e no grupo sem dor de 5 variando de 3 a 8 (p>0,05). A expressão de trk A apresentou media de 6, variandode 3 a 8, no grupo DPC, 6 no grupo DPC/ACO, variando de 4 a 8, e 6 no grupo sem dor variando de 4 a 6 (p>0,05). A expressão do RE apresentou média 4 no grupo DPC, variando de 0 a 8, 3,5 no grupo DPC/ACO variando de 0 a 8, e 7 no grupo sem dor, variando de 6 a 8 (p<0,05). A expressão do RP teve média 6,5 no grupo DPC, variando de 0 a 8, 5 no grupo DPC/ACO, variando de 0 a 7, e 7 no grupo sem dor, variando de 5 a 8 (p>0,05). Nossos resultados sugerem um papel anti-nociceptivo do estrogênio no peritôneo pélvico de mulheres no menacme, não mediado por expressão de FCN ou trk A. / Chronic pelvic pain (CPP) affects a great number of women and its management still remains complex and unsatisfactory. Studies have shown an involvement of the neuronal growing factor (NGF) in the process of permanence of pain. Hormonal participation in this process has also been put forward, as some authors have demonstrated estro/progestational influence under nociceptors direct or indirectly through their influence on neurotrofic factors. This study aimed to verify the association among the expression of neuronal growing factor (NGF), its receptor (TrKA) and the estrogen and progesterone receptors in the pelvic peritoneum with the presence of chronic pelvic pain. A transversal study was carried out including a group of 22 women with CPP, 8 with CPP and users of oral anticonceptional (CPP/OAC) and 7 without pain. The pain was analized by the visual analogic scale (VAS) and McGill\'s questionnaire. Imunehistochemical was performed to evaluate the NGF and its receptor TrKA, estrogen (ER) and progesteron (PR) receptors. The expression of NGF was an average of 5, varying from 0 to 8, in group CPP, 5,5 in group CPP/ OAC varying from 3 to 8, and in the group without pain varying from 3 to 8 (p>0,05). The expression of TrKA presented an average of 6, varying from 3 to 8, in the group CPP, 6 in the group CPP/OAC, varying from 4 to 8, and 6 in the group without pain varying from 4 to 6 (p>0,05). The expression of ER presented an average of 4 in the group CPP, varying from 0 to 8, 3,5 in group CPP/OAC varying from 0 to 8, and 7 in group without pain, varying from 6 to 8 (p<0,05). The expression of PR had an average 6,5 in the group CPP, varying from 0 to 8,5 in the group CPP/OAC, varying from 0 to 7, and 7 in the group without pain, varying from 5 to 8 (p>0,05). Our studies suggest an anti- nociceptive rule of estrogen in the pelvic peritoneum of women in menacme, not mediated by expression of NGF or TrKA.

Ambient temperature poses a significant challenge to the performance of mobile phones, impacting their internal thermal flow and increasing the likelihood of overheating, leading to a compromised user experience. The knowledge about the ambient temperature in mobile phones is crucial as it assists engineers in correlating external factors with internal factors that might affect the mobile's performance under various conditions. Notably, these devices lack dedicated sensors to measure ambient temperature independently, underscoring the need for innovative solutions to estimate it accurately.      In response to this challenge, our research investigates the feasibility of estimating ambient temperature using machine-learning algorithms based on data from internal thermal sensors in Sony mobile phones.  Through comprehensive data collection and analysis, custom datasets were constructed to simulate different use-case scenarios, including CPU workloads, camera operation, and GPU tasks. These scenarios introduced varying levels of thermal disturbance, providing a robust basis for evaluating model performance. Feature engineering played a pivotal role in ensuring that the models could effectively interpret the internal thermal dynamics and correlate them with the ambient temperature. The results demonstrate that while simpler models like Linear Regression offer computational efficiency, they fall short in scenarios with complex thermal patterns. In contrast, deep learning models, particularly those incorporating time series analysis, showed superior accuracy and robustness. The Attention-LSTM model, in particular, excelled in generalizing across diverse and novel thermal conditions, although its complexity poses challenges for on-device deployment. This research underscores the importance of selecting appropriate sensors and incorporating a wide range of training scenarios to enhance model performance. It also highlights the potential of advanced machine learning techniques in providing advance solutions for ambient temperature estimation, thereby contributing to more effective thermal management in mobile devices.

Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more conventional with different types of Convolutional Neural Networks. A segmentation network was used as well as a few detection networks and different fusions between these. The Convolutional Neural Network approach proved to be the better of the two in terms of precision and recall but the clustering approach was not far behind. This work was done using a relatively small amount of data which potentially could have impacted the results of the Machine Learning models in a negative way.

Cílem této diplomové práce je automatizace detekce vad ve vláknitých materiálech. Firma SILON se již přes padesát let zabývá výrobou jemné vaty z recyklovaných PET lahví. Tato vata se následně používá ve stavebnictví, automobilovém průmyslu, ale nejčastěji v dámských hygienických potřebách a dětských plenách. Cílem firmy je produkovat co nejkvalitnější výrobek a proto je každá dávka testována v laboratoři s několika přísnými kritérii. Jednám z testů je i množství vadných vláken, jako jsou zacuchané smotky vláken, nebo nevydloužená vlákna, která jsou tvrdá a snadno se lámou. Navrhovaný systém sestává ze snímací lavice fungující jako scanner, která nasnímá vzorek vláken, který byl vložen mezi dvě skleněné desky. Byla provedena série testů s různým osvětlením, která ověřovala vlastnosti Rhodaminu, který se používá právě na rozlišení defektů od ostatních vláken. Tyto defekty mají zpravidla jinou molekulární strukturu, na kterou se barvivo chytá lépe. Protože je Rhodamin fluorescenční barvivo, je možné ho například pod UV světlem snáze rozeznat. Tento postup je využíván při manuální detekci. Při snímání kamerou je možno si vypomoci filtrem na kameře, který odfiltruje excitační světlo a propustí pouze světlo vyzářené Rhodaminem. Součástí výroby skeneru byla i tvorba ovládacího programu. Byla vytvořena vlastní knihovna pro ovládání motoru a byla upravena knihovna pro kameru. Oba systém pak bylo možno ovládat pomocí jednotného GUI, které zajišťovalo pořizování snímku celé desky. Pomocí skeneru byla nasnímána řada snímků, které bylo třeba anotovat, aby bylo možné naučit počítač rozlišovat defekty. Anotace proběhla na pixelové úrovni; každý defekt byl označen v grafickém editoru ve speciální vrstvě. Pro rozlišování byla použita umělá neuronová síť, která funguje na principu konvolucí. Tento typ sítě je navíc plně konvoluční, takže výstupem sítě je obraz, který by měl označit na tom původním vadné pixely. Výsledky naučené sítě jsou v práci prezentovány a diskutovány. Síť byla schopna se naučit rozeznávat většinu defektů a spolehlivě je umí rozeznat a segmentovat. Potíže má v současné době s detekcí rozmazaných defektů na krajích zorného pole a s defekty, jejichž hranice není tolik zřetelná na vstupních obrazech. Nutno zmínit, že zákazník má zájem o kompletní řešení scanneru i s detekčním softwarem a vývoj tohoto zařízení bude pokračovat i po závěru této diplomové práce.