O Papel dos Estilos Educativos Parentais na Sintomatologia Ansiosa de Adolescentes do 3º Ciclo do Ensino Básico

A literatura realça a importância do impacto do comportamento parental no desenvolvimento de ansiedade em crianças e adolescentes. Dado a pertinência do tema, o foco do presente estudo visa analisar o papel que a perceção dos adolescentes sobre os estilos educativos parentais tem sobre a manifestação de sintomatologia ansiosa. A amostra desta investigação envolveu 136 adolescentes do 3º ciclo do ensino básico, 48 rapazes e 88 raparigas com idades compreendidas entre os 12 e os 15 anos, com uma média de idades de 13,2 anos, recolhida no Colégio São Martinho em Coimbra. O protocolo de investigação incluiu os seguintes instrumentos de colheita de dados: Questionário Sociodemográfico, State-Trait Anxiety Inventory for Children (STAIC) e EMBU-A. Os resultados do estudo sugerem que os adolescentes mais velhos manifestam maior sintomatologia ansiosa, estatisticamente significativa ao nível da ansiedade-estado. No que respeita ao desempenho académico, são os adolescentes com elevado insucesso escolar que exteriorizam mais ansiedade-traço. Porém, não foram encontradas diferenças significativas na manifestação da ansiedade dos adolescentes em função das variáveis género, posição na fratria e habilitações literárias dos pais. Por seu lado, em relação aos estilos educativos parentais, os jovens que têm maior insucesso escolar percecionam níveis elevados de sobreproteção da mãe, e de rejeição do pai e da mãe. Os adolescentes que têm um pai com mais baixo nível de escolaridade percecionam maior rejeição materna, e são os filhos de mães com menos habilitações literárias que sentem maior sobreproteção da mãe e rejeição do pai. Verificou-se, em particular, uma associação significativa entre a rejeição paterna e níveis mais elevados de sintomatologia ansiosa. O modelo preditivo avançado no estudo confirma que a rejeição paterna, em conjunto com a idade do adolescente, são bons preditores da sintomatologia ansiosa. Especificamente, a rejeição paterna é evidenciada como o melhor preditor da sintomatologia ansiosa, sendo o principal responsável pela manifestação de ansiedade nos adolescentes. Os resultados sugerem que a rejeição do pai desencadeia níveis elevados de sintomatologia ansiosa. Assim, este estudo permite concluir que a rejeição paterna é o estilo educativo parental que exerce maior influência na manifestação de ansiedade nos adolescentes. / The literature highlights the importance of the impact of parental behavior on the development of anxiety in children and adolescents. Given the relevance of the topic, the focus of this study is to analyze the role that the adolescents’ perception about parental rearing styles have on the manifestation of anxiety symptoms. The sample of this research involved 136 adolescents from the 3rd cycle of basic education, 48 boys and 88 girls aged between 12 and 15 years, with a mean age of 13,2 years, gathered in Colégio São Martinho in Coimbra. The investigation protocol included the following data collection instruments: Sociodemographic Questionnaire, State-Trait Anxiety Inventory for Children (STAIC) and the EMBU-A. The results of the study suggest that older adolescents show greater anxiety symptoms, statistically significant at the level of state anxiety. With regard to academic performance, are adolescents with high failure rates that externalize more trait anxiety. However, there were significant differences in the manifestation of anxiety in adolescents function of the variables gender, sibling position and educational background of the parents. For its part, in relation to parental rearing styles, young people who have higher academic failure perceive high levels of overprotection of the mother, and rejection of father and mother. Adolescents who have a father with the lowest educational level perceive greater maternal rejection, and are the children of mothers with less qualification who feel greater overprotection of the mother and father's rejection. There was, in particular, a significant association between paternal rejection and higher levels of anxiety symptoms. The predictive model advanced in the study confirms that parental rejection, together with the adolescents’ age, are good predictors of anxiety symptoms. Specifically, parental rejection is evidenced as the best predictor of anxiety symptoms, being primarily responsible for the manifestation of anxiety in adolescents. The results suggest that the father rejection triggers high levels of anxiety symptoms. Thus, this study shows that rejection is the paternal parental rearing style that has more influence on the manifestation of anxiety in adolescents.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Evaluating, predicting and mapping belowground carbon stores in Kenyan mangroves.

Despite covering only approximately 138,000 km2, mangroves are globally important carbon sinks with carbon density values 3 to 4 times that of terrestrial forests. A key challenge in evaluating the carbon benefits from mangrove forest conservation is the lack of rigorous spatially resolved estimates of mangrove sediment carbon stocks; most mangrove carbon is stored belowground. Previous work has focused on detailed estimations of carbon stores over relatively small areas, which has obvious limitations in terms of generality and scope of application. Most studies have focused only on quantifying the top 1m of belowground carbon (BGC). Carbon stored at depths beyond 1m, and the effects of mangrove species, location and environmental context on these stores, is poorly studied. This study investigated these variables at two sites (Gazi and Vanga in the south of Kenya) and used the data to produce a country-specific BGC predictive model for Kenya and map BGC store estimates throughout Kenya at spatial scales relevant for climate change research, forest management and REDD+ (Reduced Emissions from Deforestation and Degradation). The results revealed that mangrove species was the most reliable predictor of BGC; Rhizophora muronata had the highest mean BGC with 1485.5t C ha-1. Applying the species-based predictive model to a base map of species distribution in Kenya for the year 2010 with a 2.5m2 resolution, produced an estimate of 69.41 Mt C (± 9.15 95% C.I.) for BGC in Kenyan mangroves. When applied to a 1992 mangrove distribution map, the BGC estimate was 75.65 Mt C (± 12.21 95% C.I.); an 8.3% loss in BGC stores between 1992 and 2010 in Kenya. The country level mangrove map provides a valuable tool for assessing carbon stocks and visualising the distribution of BGC. Estimates at the 2.5m2 resolution provide sufficient detail for highlighting and prioritising areas for mangrove conservation and restoration.

Structure dependencies for multiscale polymer composites using extrusion processes

Multiscale reinforcement, using carbon microfibers and multi-walled carbon nanotubes, of polymer matrix composites manufactured by twin-screw extrusion is investigated for enhanced mechanical and thermal properties with an emphasis on the use of a diverging flow in the die for fluid mechanical fiber manipulation. Using fillers at different length scales (microscale and nanoscale), synergistic combinations have been identified to produce distinct mechanical and thermal behavior. Fiber manipulation has been demonstrated experimentally and computationally, and has been shown to enhance thermal conductivity significantly. Finally, a new physics driven predictive model for thermal conductivity has been developed based on fiber orientation during flow, which is shown to successfully capture composite thermal conductivity.

Identification des grands utilisateurs de soins de santé chez les patients souffrant de la douleur chronique non cancéreuse et suivis en soins de première ligne

Contexte: La douleur chronique non cancéreuse (DCNC) génère des retombées économiques et sociétales importantes. L’identification des patients à risque élevé d’être de grands utilisateurs de soins de santé pourrait être d’une grande utilité; en améliorant leur prise en charge, il serait éventuellement possible de réduire leurs coûts de soins de santé. Objectif: Identifier les facteurs prédictifs bio-psycho-sociaux des grands utilisateurs de soins de santé chez les patients souffrant de DCNC et suivis en soins de première ligne. Méthodologie: Des patients souffrant d’une DCNC modérée à sévère depuis au moins six mois et bénéficiant une ordonnance valide d’un analgésique par un médecin de famille ont été recrutés dans des pharmacies communautaires du territoire du Réseau universitaire intégré de santé (RUIS), de l’Université de Montréal entre Mai 2009 et Janvier 2010. Ce dernier est composé des six régions suivantes : Mauricie et centre du Québec, Laval, Montréal, Laurentides, Lanaudière et Montérégie. Les caractéristiques bio-psycho-sociales des participants ont été documentées à l’aide d’un questionnaire écrit et d’une entrevue téléphonique au moment du recrutement. Les coûts directs de santé ont été estimés à partir des soins et des services de santé reçus au cours de l’année précédant et suivant le recrutement et identifiés à partir de la base de données de la Régie d’Assurance maladie du Québec, RAMQ (assureur publique de la province du Québec). Ces coûts incluaient ceux des hospitalisations reliées à la douleur, des visites à l’urgence, des soins ambulatoires et de la médication prescrite pour le traitement de la douleur et la gestion des effets secondaires des analgésiques. Les grands utilisateurs des soins de santé ont été définis comme étant ceux faisant partie du quartile le plus élevé de coûts directs annuels en soins de santé dans l’année suivant le recrutement. Des modèles de régression logistique multivariés et le critère d’information d’Akaike ont permis d’identifier les facteurs prédictifs des coûts directs élevés en soins de santé. Résultats: Le coût direct annuel médian en soins de santé chez les grands utilisateurs de soins de santé (63 patients) était de 7 627 CAD et de 1 554 CAD pour les utilisateurs réguliers (188 patients). Le modèle prédictif final du risque d’être un grand utilisateur de soins de santé incluait la douleur localisée au niveau des membres inférieurs (OR = 3,03; 95% CI: 1,20 - 7,65), la réduction de la capacité fonctionnelle liée à la douleur (OR = 1,24; 95% CI: 1,03 - 1,48) et les coûts directs en soins de santé dans l’année précédente (OR = 17,67; 95% CI: 7,90 - 39,48). Les variables «sexe», «comorbidité», «dépression» et «attitude envers la guérison médicale» étaient également retenues dans le modèle prédictif final. Conclusion: Les patients souffrant d’une DCNC au niveau des membres inférieurs et présentant une détérioration de la capacité fonctionnelle liée à la douleur comptent parmi ceux les plus susceptibles d’être de grands utilisateurs de soins et de services. Le coût direct en soins de santé dans l’année précédente était également un facteur prédictif important. Améliorer la prise en charge chez cette catégorie de patients pourrait influencer favorablement leur état de santé et par conséquent les coûts assumés par le système de santé.

Influência do género, da institucionalização e da força da fé sobre a ansiedade face à morte em adultos idosos

Dissertação de Mestrado apresentada ao Instituto Superior de Psicologia Aplicada para obtenção de grau de Mestre na especialidade de Psicologia Clínica.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Causal factors of anxiety symptoms in children

Aim: The present work aimed to investigate the impact of the child’s cognitions associated with ambiguous stimuli that refer to anxiety, both parents’ fears and anxiety, and parents’ attributions to the child’s interpretations of ambiguous stimuli on child anxiety. The influence of parental modelling on child’s cognitions was also analyzed. Method: The final sample was composed of 111 children (62 boys; 49 girls) with ages between 10 and 11 years (M = 10.6, SD = 0.5) from a community population, and both their parents. The variables identified as most significant were included in a predictive model of anxiety. Results: Results revealed the children’s thoughts (positive and negative) related to ambiguous stimuli that describe anxiety situations. Parents’ fears and mothers’ anxiety significantly predict children’s anxiety. Those variables explain 29% of the variance in children general anxiety. No evidence was found for a direct parental modeling of child cognitions. Conclusion: Children’s positive thoughts seem to be cognitive aspects that buffer against anxiety. Negative thoughts are vulnerability factors for the development of child anxiety. Parents’ fears and anxiety should be analyzed in separate as they have distinct influences over children’s anxiety. Mothers’ fears contribute to children’s anxiety by reducing it, revealing a possible protective effect. It is suggested that the contribution of both parents’ fears to children’s anxiety may be interpreted acknowledging the existence of “psychological and/or behavioral filters”. Mothers’ filters seem to be well developed while fathers’ filters seem to be compromised. The contribution of mothers’ anxiety (but not fathers’ anxiety) to children’s anxiety is also understood in light of the possible existence of a “proximity space” between the child and parents, which is wider with mothers than with fathers.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Purpose: The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Methods: Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. Results: The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911–0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Conclusions: Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.

Predicting the concentration of residual methanol in industrial formalin using machine learning

In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to provide the best results and was used to create the predictive model. The model was compiled to a stand-alone application with a graphical user interface using the MATLAB CompilerTM.

Factores asociados a transfusión sanguínea en el manejo quirúrgico del canal lumbar estrecho en la Fundación Santa Fe de Bogotá 2003 - 2013

Introduccion: El canal lumbar estrecho es un motivo de consulta frecuente en el servicio de columna de la Fundación Santa Fe de Bogotá. Derivado del tratamiento quirurgico se pueden generar múltiples complicaciones, entre las que se encuentra la transfusión sanguínea. Objetivo: Identificar los factores sociodemográficos, antecedentes personales y factores quirúrgicos asociados a transfusión sanguínea en cirugía canal lumbar estrecho en la Fundación Santa Fe de Bogotá 2003- 2013. Materiales y métodos: Se aplicó en diseño de estudio observacional analítico transversal. Se incluyeron 367 pacientes sometidos a cirugía de canal lumbar estrecho a quienes se les analizaron variables de antecedentes personales, características sociodemograficas y factores quirúrgicos. Resultados: La mediana de la edad fue de 57 años y la mayoría de pacientes fueron mujeres (55,6%). La mediana del Índice de Masa Corporal (IMC) fue de 24,9 clasificado como normal. Entre los antecedentes patológicos, la hipertensión arterial fue el más común (37,3%). La mayoría de pacientes (59,1%) presentaron clasificación ASA de II. El tipo de cirugía más prevalente fue el de descompresión (55,6%). En el 79,8% de los pacientes se intervinieron 2 niveles. Se realizó transfusión de glóbulos rojos en 26 pacientes correspondiente a 7,1% del total. En la mayoría de procedimientos quirúrgicos (42,5%) el sangrado fue clasificado como moderado (50-500 ml). En el modelo explicativo transfusión sanguínea en cirugía de canal lumbar estrecho se incluyen: antecedente de cardiopatía (OR 4,68, P 0,034, IC 1,12 – 19,44), Sangrado intraoperatorio >500ml (OR 6,74, p 0,001, 2,09 – 21,74) y >2 niveles intervenidos (OR 3,97, p 0,023, IC 1,20 – 13,09). Conclusión: Como factores asociados a la transfusión sanguínea en el manejo quirúrgico del canal lumbar estrecho a partir de la experiencia de 10 años en la Fundación Santa Fe de Bogotá se encontraron: enfermedad cardiaca, sangrado intraoperatorio mayor de 500ml y más de dos niveles intervenidos.

Modelo predictivo para uso del condón y consumo de alcohol como conductas de riesgo relacionadas con el contagio de VIH/SIDA en trabajadoras sexuales de Bogotá- 2015

Objetivo: Determinar un modelo predictivo para uso del condón y consumo de alcohol como conductas de riesgo relacionadas el contagio de VIH/Sida en mujeres trabajadoras sexuales de la ciudad de Bogotá en el año 2015. Métodos Estudio de tipo transversal con diseño observacional, se tomaron 255 mujeres trabajadoras sexuales de la ciudad de Bogotá; La información analizada fue tomada del estudio realizado en cinco ciudades de Colombia en el año 2015, las hipótesis planteadas se soportaron en la asociación entre las condiciones sociodemográficas, de conocimiento, practicas, hábitos, apoyo social y de ocupación propia de las mujeres trabajadoras sexuales que podían explicar y predecir la adopción de conductas riesgosas para VIH/sida como son el uso del condón y el consumo de alcohol en ejercicio de su ocupación. Resultados El promedio de edad de inicio en el trabajo sexual fue 22,1±7,1 años, tres cuartas partes son solteras y residen en estrato dos y tres; el 96,5% dijo usar el condón con el último cliente y el 27,8% de ellas consumió alcohol durante su último servicio. En la conducta de riesgo uso del condón, se encontraron asociados entre otras, la edad [OR=1,10(1,03-1,17)], vivir en estrato dos [OR=7,7(1,5-39,5)], el ingreso por trabajo sexual [OR=1,0(1,0-1,0)], la disponibilidad del condón para el servicio [OR=0,03(0,008-0,16)] y contar con otro método de planificación (ligadura de trompas) [OR=4,47(1,0-18,3)]. En la conducta de riesgo consumo de alcohol, se encontró asociado ente otros: estrato socioeconómico dos [OR=5,8(1,54-22,3)], nivel de escolaridad secundaria [OR=0,12(0,16-0,96)], vivir con otros familiares [OR=3,45(1,7-7,02)], ingreso por trabajo sexual [OR=1,0(1,0-1,0)] y el sitio donde se ofrece el servicio [OR=0,07(0,04-0,15)]. Después de ajustar, se encontró que las variables que mejor explican el uso del condón fueron edad [OR=1,1(1,02-1,17)] y disponibilidad del condón [OR=0,04(0,008-0,024)], el modelo tuvo poca sensibilidad 33,3% y buena capacidad predictiva (84,6%). Las variables que mejor explicaron el consumo de alcohol durante el servicio fueron edad [OR= 0,95(0,91-0,98)], Número de clientes por semana [OR=0,9(0,90-0,98)], sitio donde ofrece el servicio [OR=7,1(3,45-14,8)], y estrato socioeconómico [OR=1,8 (0,90-3,83)], resultando un modelo con buena sensibilidad (71,8%) y buena capacidad predictiva (86,4%). Conclusiones Aspectos como la edad, el estrato socioeconómico, escolaridad, estado civil, ingreso económico por trabajo sexual, edad de inicio en el trabajo sexual, número de clientes antiguos en la última semana, disponibilidad del condón para prestar el servicio y ligadura de trompas como método diferente de planificación, se asociaron estadísticamente con el uso del condón. Sin embargo al ajustar las variables solo la edad y la disponibilidad del condón se mantuvieron como variables explicativas. Cabe anotar, que aunque el modelo mostró buena capacidad predictiva (84,6%), la precisión en sus estimaciones fue baja debido a la poca frecuencia del no uso del condón con el ultimo cliente (3,5%), y la sensibilidad del modelo apenas fue del 33,3%. Por otro lado, factores como la edad, el estrato socioeconómico, nivel educativo, ingreso económico, sitio de oferta del servicio, composición familiar, número de hijos, número de clientes atendidos en la última semana y número de clientes antiguos mostraron asociación estadística con el consumo de alcohol. Sin embargo, al ajustar las variables solo edad, estrato socioeconómico, sitio donde se ofrece el servicio y número de clientes por semana mantuvieron asociación estadística; observándose además que el estrato socioeconómico (uno y dos) y sitio donde se ofrece el servicio (establecimiento), son factores de riesgo para el consumo de alcohol en ejercicio de la ocupación y la poca edad y un número reducido de clientes por semana se comportan como factores de protección para el consumo de alcohol. El modelo predictivo que se desarrolló para la conducta de riesgo de consumo de alcohol, con una sensibilidad del 71,8% y un poder predictivo del 86,4%. .

Constraint Programming-based Job Dispatching for Modern HPC Applications

A High-Performance Computing job dispatcher is a critical software that assigns the finite computing resources to submitted jobs. This resource assignment over time is known as the on-line job dispatching problem in HPC systems. The fact the problem is on-line means that solutions must be computed in real-time, and their required time cannot exceed some threshold to do not affect the normal system functioning. In addition, a job dispatcher must deal with a lot of uncertainty: submission times, the number of requested resources, and duration of jobs. Heuristic-based techniques have been broadly used in HPC systems, at the cost of achieving (sub-)optimal solutions in a short time. However, the scheduling and resource allocation components are separated, thus generates a decoupled decision that may cause a performance loss. Optimization-based techniques are less used for this problem, although they can significantly improve the performance of HPC systems at the expense of higher computation time. Nowadays, HPC systems are being used for modern applications, such as big data analytics and predictive model building, that employ, in general, many short jobs. However, this information is unknown at dispatching time, and job dispatchers need to process large numbers of them quickly while ensuring high Quality-of-Service (QoS) levels. Constraint Programming (CP) has been shown to be an effective approach to tackle job dispatching problems. However, state-of-the-art CP-based job dispatchers are unable to satisfy the challenges of on-line dispatching, such as generate dispatching decisions in a brief period and integrate current and past information of the housing system. Given the previous reasons, we propose CP-based dispatchers that are more suitable for HPC systems running modern applications, generating on-line dispatching decisions in a proper time and are able to make effective use of job duration predictions to improve QoS levels, especially for workloads dominated by short jobs.

Outcome analysis of predictors in locally advanced pancreatic cancer

Guidelines report a wide range of options in locally advanced pancreatic cancer (LAPC): definitive chemotherapy or chemoradiotherapy or the emerging stereotactic body radiotherapy (SBRT) (+/- chemotherapy). On behalf of the AIRO (Italian Association of Radiation Oncology and Clinical Oncology) Gastrointestinal Study Group, we collected retrospective clinical data on 419 LAPC from 15 Italian centers. The study protocol (PAULA-1: Pooled Analysis in Unresectable Locally Advanced pancreatic cancer) was approved by institutional review board of S. Orsola-Malpighi Hospital (201/2015/O/OssN). From this large database we performed tree different studies. The first was a retrospective study about 56 LAPC treated with SBRT at a median biologically equivalent dose of 48 Gy +/- chemotherapy. We demonstrated a statistically significant impact of biologically equivalent dose based on an α/β ratio of 10Gy ≥ 48Gy for local control (LC) (p: 0.045) and overall survival (p: 0.042) in LAPC. The second was a retrospective matched-cohort case-control study comparing SBRT (40 patients) and chemoradiation (40 patients) in LAPC in terms of different endpoints. Our findings suggested an equivalence in terms of most outcomes among the two treatments and an advantage of SBRT in terms of LC (p: 0.017). The third study was a retrospective comparison of definitive chemotherapy, chemoradiotherapy and SBRT (+/- chemotherapy) in terms of different outcomes in LAPC. A predictive model for LC in LAPC was also developed reaching an AUC of 68% (CI 58,7%-77,4%). SBRT treatment emerged as a positive predictive factor for improved LC. Findings deriving from our three studies suggest that SBRT is comparable to standard of care (definitive chemotherapy and chemoradiotherapy) in terms of outcomes. SBRT seems to be an emerging therapeutic option in LAPC significantly improving local control. Furthermore, we have shown the potential of a predictive model for LC. Randomized trials are needed to compare these different therapeutic options in LAPC.

Risk assessment to improve food safety along different food chains

The great challenges of today pose great pressure on the food chain to provide safe and nutritious food that meets regulations and consumer health standards. In this context, Risk Analysis is used to produce an estimate of the risks to human health and to identify and implement effective risk-control measures. The aims of this work were 1) describe how QRA is used to evaluate the risk for consumers health, 2) address the methodology to obtain models to apply in QMRA; 3) evaluate solutions to mitigate the risk. The application of a QCRA to the Italian milk industry enabled the assessment of Aflatoxin M1 exposure, impact on different population categories, and comparison of risk-mitigation strategies. The results highlighted the most sensitive population categories, and how more stringent sampling plans reduced risk. The application of a QMRA to Spanish fresh cheeses evidenced how the contamination of this product with Listeria monocytogenes may generate a risk for the consumers. Two risk-mitigation actions were evaluated, i.e. reducing shelf life and domestic refrigerator temperature, both resulting effective in reducing the risk of listeriosis. A description of the most applied protocols for data generation for predictive model development, was provided to increase transparency and reproducibility and to provide the means to better QMRA. The development of a linear regression model describing the fate of Salmonella spp. in Italian salami during the production process and HPP was described. Alkaline electrolyzed water was evaluated for its potential use to reduce microbial loads on working surfaces, with results showing its effectiveness. This work showed the relevance of QRA, of predictive microbiology, and of new technologies to ensure food safety on a more integrated way. Filling of data gaps, the development of better models and the inclusion of new risk-mitigation strategies may lead to improvements in the presented QRAs.