The master's thesis deals with new construction Salesian center, which contents a leisure center and Church of Mary Help of Christians. The building is designed as two separate units divided with dilatation cleft. One building is reinforced concrete frame fill with masonry POROTHERM Profi 40, the second object was built from POROTHERM Profi 40. The whole building has a basement, the basement is designed as a monolithic reinforced concrete construction. Basement is insulated with extruded polystyrene thickness of 150 mm, upper building is insulated EPS polystyrene 70 mm thick and 100 mm thick ETICS system. The roof on both buildings is flat, smaller building is covered with vegetation roof, concrete frame is covered with a flat roof with asphalt sheets. There are prepared the study, complete design documentation, technical expertise, heat, fire safety solution construction including fire management and technical drawings, basic acoustic calculations of structures, the reverberation time in the nave of the church. Drawings were prepared using AutoCAD 2009 and technical expertise using Heat 2011 and Area 2011.

The theme of this work is the building design, brand new mountain hotel with wellness services in Spindleruv Mlyn. The plot is located in a quiet part of town, near a wooded area and form a river Labe.V designed hotel is located three floors above ground level of the building is solved attic space. Roofed by a wooden roof false hip roof. Ground unsophisticated form a geometric shape, which layout is divided into sections. On the ground floor wellness facilities, a portion of the input dopňkovými with hotel services, floor is done mainly for accommodation, as well as the attic. Hotel is equipped with a slightly sloping plot in the construction of houses and other hotels. All living spaces are naturally ventilated and illuminated. The supporting structure consists Liapor system. The appearance of the hotel is in line with the surroundings.

Les infections virales des voies respiratoires (IVVRs) causées par certains virus comme la grippe et le COVID-19 ont un impact significatif sur la santé publique et l’économie mondiale. Ces infections touchent un nombre important de personnes dans le monde et exercent une pression immense sur les systèmes de santé. Pour atténuer les effets néfastes des IVVRs, il est important de développer des techniques de détection précoce capables d’identifier les personnes infectées même si elles ne présentent aucun symptôme. Une telle détection permet un isolement et raitement rapide, ce qui réduit le risque de transmission et permet des interventions de santé publique ciblées pour limiter la propagation de l’infection. Les méthodes de détection actuelles telles que la réaction en chaîne par polymérase (RCP) démontrent une sensibilité et une spécificité élevées, atteignant des taux de détection de 100% avec certaines méthodes de test disponibles dans le marché. De plus, les approches actuelles d’apprentissage automatique pour la détection des IVVRs, montrent des résultats prometteurs ; cependant, les méthodes actuelles reposent souvent sur l’apparition des symptômes, exigent un équipement coûteux et un personnel formé, et fournissent des résultats relativement retardés. Notre projet vise à étudier la faisabilité de l’utilisation d’un algorithme d’apprentissage automatique entraîné sur des données physiologiques provenant de biocapteurs portables lors d’un protocole de test de marche sur escalier pour prédire le niveau d’inflammation associé aux IVVRs. De plus, l’étude vise à identifier les indicateurs les plus prédictifs des IVVRs. Des participants en bonne santé ont été recrutés et inoculés avec un vaccin antigrippal vivant pour induire une réponse immunitaire. Au cours d’une série de tests d’escalier contrôlés cliniquement, des physiomarqueurs tels que la fréquence respiratoire et la fréquence cardiaque ont été meusurés à l’aide de biocapteurs portables. Les données collectées ont été utilisées pour développer un modèle de prédiction en ayant recours aux algorithmes d’apprentissage automatique, combinés avec un réglage d’hyperparamètres et en écartant un participant à la fois lors de l’entraînement du modèle. L’étude a développé avec succès un modèle prédictif qui démontre des résultats prometteurs dans la prédiction du niveau d’inflammation lié au vaccin induit. Notamment, les caractéristiques de variabilité de la fréquence cardiaque (VFC) dérivées du biocapteur portable présentaient le potentiel le plus élevé pour détecter le niveau d’inflammation, atteignant une sensibilité de 70% et une spécificité de 77%. Les implications du modèle de prédiction développé sont importantes pour les cliniciens et le grand public, notamment en termes d’autosurveillance et d’intervention précoce. Grâce aux algorithmes d’apprentissage automatique et des physiomarqueurs utilisés, en particulier les caractéristiques de VFC, cette approche a le potentiel de faciliter l’administration en temps opportun des traitements appropriés, atténuant ainsi l’impact des futures épidémies des IVVRs. L’intégration de biocapteurs portables et d’algorithmes d’apprentissage automatique fournit une stratégie innovante et efficace de détection précoce, permettant une intervention rapide et réduisant la charge sur les systèmes de santé / Viral respiratory tract infections (VRTIs) caused by certain viruses like influenza and COVID-19, significantly impact public health and the global economy. These infections affect a large number of people worldwide and put immense pressure on healthcare systems. To mitigate the detrimental effects of VRTIs, it is crucial to urgently develop accurate early detection techniques that can identify infected individuals even if they do not exhibit any symptoms. Timely detection allows for prompt isolation and treatment, reducing the risk of transmission and enabling targeted public health interventions to limit the spread of the infection. Current detection methods like polymerase chain reaction (PCR) demonstrate high sensitivity and specificity, reaching 100% detection rates with some commercially available testing methods. Additionally, current machine learning approaches for automatic detection show promising results; however, current methods often rely on symptom onset, demand expensive equipment and trained personnel, and provide delayed results. This study aims to investigate the feasibility of utilizing a machine learning algorithm trained on physiological data from wearable biosensors during a stair stepping task protocol to predict the level of inflammation associated with VRTIs. Additionally, the study aims to identify the most predictive indicators of VRTIs. Healthy participants were recruited and inoculated with a live influenza vaccine to induce an immune response. During a series of clinically controlled stair tests, physiomarkers such as breathing rate and heart rate were monitored using wearable biosensors. The collected data were employed to develop a prediction model through the utilization of gradient boosting machine learning algorithms, which were combined with hyperparameter tuning and a leave-one-subject-out approach for training. The study successfully developed a predictive model that demonstrates promising results in predicting the level of inflammation related to the induced VRTI. Notably, heart rate variability (HRV) features derived from the wearable biosensor exhibited the highest potential in detecting the level of inflammation, achieving a sensitivity of 70% and a specificity of 77%. The implications of the developed prediction model are significant for clinicians and the general public, particularly in terms of self-monitoring and early intervention. By leveraging machine learning algorithms and physiomarkers, specifically HRV features, this approach holds the potential to facilitate the timely administration of appropriate treatments, thereby mitigating the impact of future VRTI outbreaks. The integration of wearable biosensors and machine learning algorithms provides an innovative and effective strategy for early detection, enabling prompt intervention and reducing the burden on healthcare system

Background: Knee osteoarthritis is a degenerative disease characterized by damaged joint tissues (e.g., cartilage) that leads to joint pain, and reduced mobility and quality of life. Various factors are involved in disease progression, including biomechanical, patient-reported outcome and mobility measures. This thesis provides important longitudinal data on the role of these factors in disease progression, and the trajectory of biomechanical factors in persons with knee osteoarthritis. Objectives: (1) Determine the extent to which changes over 2.5 years in knee cartilage thickness and volume in persons with knee osteoarthritis were predicted by the knee adduction and flexion moment peaks, and knee adduction moment impulse and loading frequency. (2) Determine the extent to which changes over 2 years in walking and stair-climbing mobility in women with knee osteoarthritis were predicted by quadriceps strength and power, pain and self-efficacy. (3) Estimate the relative and absolute test-retest reliabilities of biomechanical risk factors for knee osteoarthritis progression. Methods: Data were collected at 3-month intervals during a longitudinal (3-year), observational study of persons with clinical knee osteoarthritis (n=64). Magnetic resonance imaging of the study knee was acquired at the first and last assessments, and used to determine cartilage thickness and volume. Accelerometry and dynamometry data were acquired every 3 months, and used to determine knee loading frequency and knee muscle strength and power, respectively. Walking and stair-climbing mobility, as well as pain and self-efficacy data, were also collected every 3 months. Gait analyses were performed every 6 months, and used to calculate lower-extremity kinematics and kinetics. Results: (1) The knee adduction moment peak and impulse each interacted with body mass index to predict loss of medial tibial cartilage volume over 2.5 years. These interactions suggested that larger joint loads in those with a higher body mass index were associated with greater loss of cartilage volume. (2) In women, lower baseline self-efficacy predicted decreased walking and stair ascent performances over 2 years. Higher baseline pain intensity/frequency also predicted decreased walking performance. Quadriceps strength and power each interacted with self-efficacy to predict worsening stair ascent times. These interactions suggested that the impact of lesser quadriceps strength and power on worsening stair ascent performance was more important among women with lower self-efficacy. (3) Relative reliabilities were high for the knee adduction moment peak and impulse, quadriceps strength and power, and body mass index (i.e., intraclass correlation coefficients >0.80). Absolute reliabilities were high for quadriceps strength and body mass index (standard errors of measurement <15% of the mean). Data supported the use of interventions effective in reducing the knee adduction moment and body mass index, and increasing quadriceps strength, in persons with knee osteoarthritis. Conclusion: Findings from this thesis suggest that biomechanical factors play a modest independent role in the progression of knee osteoarthritis. However, in the presence of other circumstances (e.g., obesity, low self-efficacy, high pain intensity/frequency), biomechanical factors can vastly worsen the disease. Strategies aiming to curb structural progression and improve clinical outcomes in knee osteoarthritis should target biomechanical and clinical outcomes simultaneously. / Thesis / Doctor of Philosophy (PhD) / Knee osteoarthritis is a multifactorial disease whose progression involves worsening joint structure, symptoms, and mobility. Various factors are linked to the progression of this disease, including biomechanical, patient-reported outcome and mobility measures. This thesis provides important information on how these factors, separately and collectively, are involved in worsening disease over time, as well as benchmarks that are useful to clinicians and researchers in interpreting results from interventional or longitudinal research. First, we examined how different elements of knee loading were associated with changes in knee cartilage quantity over time in persons with knee osteoarthritis. Second, we examined how different elements of knee muscle capacity and patient-reported outcomes were related to changes in mobility over time in persons with knee osteoarthritis. Third, we examined the stability over time of various biomechanical risk factors for the progression of knee osteoarthritis. Novel results from this thesis showed that: (1) larger knee loads predicted cartilage loss over 2.5 years in obese individuals with knee osteoarthritis but not in persons of normal weight or overweight; (2) among women with knee osteoarthritis with lower self-efficacy (or confidence), lesser knee muscle capacity (strength, power) was an important predictor of declining stair-climbing performance over 2 years; and (3) clinical interventions that can positively alter knee biomechanics include weight loss, knee muscle strengthening, as well as specific knee surgery and alterations during walking to reduce knee loads. Interventions for knee osteoarthritis should target biomechanical and clinical outcomes simultaneously.

The project solves a multifunctional Duma building in a vacant lot, contemplated the construction site is located in Carlsbad, in the street Vyhlíce. This is a protected site spa. Part of the project's layout and structural design of the house. It is a six-storey house with an attic and a basement floor. It is designed as a free-standing in the gap as the final house. The layout is divided into two complete units with their own input. There are spaces for business and residential units for permanent housing. Part of the living area are also room house equipment. Inputs to both parts are wheelchair accessible. The house is not wheelchair The house is designed as a brick building of brick masonry Porotherm the module dimensions of 250 (125) mm with reinforced concrete ceilings. Roofed by a hipped roof. The house is located on a private plot of 519 m2 built-up area of 221 m2. The land is gently sloping. The main orientation of the building to the cardinal's east and west. The south wall is adjacent to the neighboring house.