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Background: Financial constraints challenge evidence of the effectiveness of dermatological inpatient management. Objective: To evaluate the effectiveness of hospitalization in atopic dermatitis and psoriasis regarding initial and sustained benefits. Methods: Prospective study on adults with psoriasis vulgaris (n = 22) and atopic dermatitis (n = 14). At admission, discharge, and 3 months after discharge, validated outcomes of objective and subjective disease severity were assessed by trained investigators. Results: Hospitalization resulted in substantial benefit in quality of life and clinical disease severity. Looking at mean scores, the observed benefit appeared stable until 3-month follow-up. The analysis of individual patient data revealed significant changes in disease severity between discharge and 3-month follow-up with some patients relapsing, others further improving. Reasons for hospitalization and treatment performed were not related to sustained benefit. Conclusions: In psoriasis vulgaris and atopic dermatitis, hospitalization effectively improved quality of life and clinical disease severity. Further research should focus on prognostic factors for sustained improvement. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

In my dissertation, I investigate the relationship between household consumption expenditures and transitory income shocks. In the first two essays, I pay particular attention to household expenditures in the aftermath of natural disasters, which are becoming more frequent and costly in the U.S. since 1980. Additionally, I study specialty farm producers' risk attitudes after an income shock due to natural disasters. Although the permanent income hypothesis predicts that households smooth consumption over their lifetimes, credit-constrained households may find consumption smoothing impractical. This dissertation brings forth evidence regarding heterogeneity in the effect of income shocks on household expenditures. First, I find that floods and hurricanes affect food-at-home (FAH) spending in different ways. The average 15-day decrease in FAH spending is about $2 in the 90 days after a flood and about $7 in the 30 days after a hurricane. In other words, floods have a prolonged effect and hurricanes have an immediate effect. I find that floods and hurricanes remain a threat to the FAH expenditures of vulnerable households, for instance, low-income households and households in coastal states. Second, Indiana specialty farm households reduce their monthly expenses of food and miscellaneous categories by about $119 and $280, respectively, after an income loss of 20%-32%. I also find that Indiana specialty producers are less willing to take financial risk after an income loss experience, i.e., they have a decreasing absolute risk aversion. Finally, in the third essay, I show that Australian households exhibit loss aversion in consumption expenditures which also means that they behave asymmetrically in their consumption response to income shocks. However, it is only working-age younger households that show asymmetric consumption behavior as opposed to the symmetric behavior of retirement-age households. The main message of these various findings is clear: after an income shock, the magnitude of change in consumption expenditures and the saliency of certain expenditure categories for adjustment are context- and population-dependent. Hence, income support policies and post-disaster relief programs may benefit from a better understanding of the consumption behavior of beneficiary population, to achieve maximum impact through better targeting.

This study investigated the associations between sleep patterns and singing voice quality in 231 adult singers of various skill levels across the United States. The four-part survey using a general questionnaire on demographics, musical background, vocal health, and three established survey instruments: the Pittsburgh Sleep Quality Index (PSQI), the Singing Voice Handicap Index-10 (SVHI-10), and the Epworth Sleepiness Scale (ESS) found that while scores were worse than normative values for the PSQI and the SVHI-10, a Pearson correlation between the two showed a moderate association. A linear regression also yielded that 8.9% of the variance in SVHI-10 scores could be predicted from PSQI scores. While further research is needed in this area, this study suggests that the amount of sleep needed for an optimal singing voice may be different from the amount needed to feel well-rested for some singers. Moreover, singers may overestimate the influence of sleep on their singing voices.

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SyfteUndersöka påverkan av kronisk orofacial smärta och psykologiska faktorer på sömnkvalitet vid käkmuskelmyalgi.Material och metoderDenna retrospektiva studie omfattade 37 patienter (6 män, 31 kvinnor, medelålder: 49 år) med käkmuskelmyalgi. Sömnkvalitet (Pittsburgh Sleep Quality Index), smärtintensitet och smärtrelaterad funktionsnedsättning (Graded Chronic Pain Scale), depression (Patient Health Questionnaire-9), ångest (General Anxiety Disorder-7), stress (Perceived Stress Scale-10) och katastrofiering (Patient Catastrophizing Scale) undersöktes med varierade formulär. Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) användes för att identifiera patienter med myalgi i käkmuskulatur.Resultat75% av patienterna rapporterade dålig sömnkvalitet, 73% rapporterade minst mild depressionsgrad, 54% rapporterade minst mild ångest, 59% rapporterade måttlig stressnivå och 38% rapporterade kliniskt relevant katastrofiering. Försämrad sömnkvalitet var relaterad till depression (rs = 0.45, n = 37, p = 0.008) ångest (rs = 0.46, n = 37, p = 0.007), stress (rs = 0.43, n = 37, p = 0.014) och katastrofiering (rs = 0.37, n = 37, p = 0.034). Multivariat logistisk regression visade att smärtintensitet, smartrelaterad funktionsnedsättning, depression, ångest, stress, katastrofiering och antal käkmuskler med refererad palpationssmärta förklarade dålig sömnkvalitet signifikant (p = 0.031).KonklusionSömnkvaliteten hos patienter med käkmuskelmyalgi påverkas i hög grad av kronisk smärtintensitet, smärtrelaterad funktionsnedsättning, antal käkmuskler med refererad palpationssmärta och depression samt ångest, stress och katastrofiering. / AimInvestigate the influence of chronic orofacial pain and psychological factors on sleep quality in patients with myalgia of the masticatory muscles.Material and methodsThis retrospective study included 37 patients (6 men, 31 women, mean age: 49 years) with masticatory muscle myalgia. Sleep quality (Pittsburgh Sleep Quality Index), pain intensity and pain-related disability (Graded Chronic Pain Scale), depression (Patient Health Questionnaire-9), anxiety (General Anxiety Disorder-7), stress (Perceived Stress Scale-10) and catastrophizing (Patient Catastrophizing Scale) were assessed by questionnaires. The Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) were used to identify patients with myalgia.Results75% of the patients reported poor sleep quality, 73% reported at least mild depression degree, 54% reported at least mild degree of anxiety, 59% reported at least a moderate stress level and 38% reported a clinically relevant degree of catastrophizing. Impaired sleep quality was related to degree of depression (rs = 0.45, n = 37, p = 0.008), anxiety (rs = 0.46, n = 37, p = 0.007), stress (rs = 0.43, n = 37, p = 0.014) and catastrophizing (rs = 0.37, n = 37, p = 0.034). Multivariate logistic regression showed that characteristic pain intensity, degree of pain-related disability, depression, anxiety, stress, catastrophizing and number of masticatory muscle sites with referred pain significantly explained poor sleep quality (p = 0.031).ConclusionSleep quality in patients with masticatory myalgia is influenced by chronic pain intensity and related disability, number of masticatory muscle sites with referred pain as well as depression, anxiety, stress and catastrophizing.

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Esta pesquisa de doutorado faz uma avaliação correlacional entre a evolução espacial da rede de monitoramento de qualidade das águas interiores do Estado de São Paulo, através do número de pontos de amostragem e sua respectiva densidade espacial ao longo dos 30 anos de existência da mesma e o crescimento populacional, através da densidade populacional dos municípios inseridos nas 22 unidades de gerenciamento de recursos hídricos do Estado de São Paulo. Analisa também se estes pontos mantiveram a capacidade de representar o status da qualidade da água em função do crescimento populacional, e se é necessário expandir ou adensar a rede em determinadas regiões. Esta comparação ficou mais racional com o uso dos recursos das geotecnologias e da análise multicritério aplicada ao planejamento e gerenciamento de recursos hídricos, com a construção do SIG SP_WATERNET e através da criação de um índice de avaliação do monitoramento que relaciona as 22 unidades espacialmente e ao longo do período estudado. Este índice pode ressaltar o grau de abrangência e de vulnerabilidade da rede de monitoramento das águas interiores superficiais no Estado de São Paulo. / This Ph.D. research makes a correlational evaluation between the spatial evolution of the monitoring network of inland surface waters of State of São Paulo, through the number of sampling points and its density throughout the 30 years of existence of same and the population growth (urbanization) and the population density in the 22 units of water management units of the São Paulo State. It also analyzes if these points had kept the capacity to represent the status of the quality of the water in function of the population growth, and if it is necessary to expand or to rearrange the network in some regions. This comparison was more rational with the use of the resources of the geo-information applied for water resources planning and management, with the construction of SIG SP_WATERNET and with the proposal of an multi-criteria evaluation monitoring index concerning the 22 water management units throughout a studied period. This index can point out the coverage or the vulnerability of the monitoring efficiency of the superficial waters network of São Paulo State.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)

Esta dissertação apresenta os resultados do estudo de monitoramento da qualidade de água na região hidrográfica da Baixada de Jacarepaguá através de coletas e posterior análise laboratorial realizadas na bacia hidrográfica experimental e representativa do Rio Morto. A bacia possui características predominantes peri-urbanas. / This thesis presents the results of the monitoring study of water quality in the river basin district of Jacarepagua marshland through collections and further laboratory analysis conducted in experimental and representative basin of the Dead River. The basin has peri- urban predominant features .