Multiple solutions and numerical analysis to the dynamic and stationary models coupling a delayed energy balance model involving latent heat and discontinuous albedo with a deep ocean

We study a climatologically important interaction of two of the main components of the geophysical system by adding an energy balance model for the averaged atmospheric temperature as dynamic boundary condition to a diagnostic ocean model having an additional spatial dimension. In this work, we give deeper insight than previous papers in the literature, mainly with respect to the 1990 pioneering model by Watts and Morantine. We are taking into consideration the latent heat for the two phase ocean as well as a possible delayed term. Non-uniqueness for the initial boundary value problem, uniqueness under a non-degeneracy condition and the existence of multiple stationary solutions are proved here. These multiplicity results suggest that an S-shaped bifurcation diagram should be expected to occur in this class of models generalizing previous energy balance models. The numerical method applied to the model is based on a finite volume scheme with nonlinear weighted essentially non-oscillatory reconstruction and Runge–Kutta total variation diminishing for time integration.

Credit risk & forward price models

Doutoramento em Gestão

Comparison of multiple intelligences in children from schools with different pedagogical models

The purpose of this research was to apply a test that measures different multiple intelligences in children from two different elementary schools to determine whether there are differences between the Academicist Pedagogical Model (traditional approach) established by the Costa Rican Ministry of Public Education and the Cognitive Pedagogical Model (MPC) (constructivist approach). A total of 29 boys and 20 girls with ages 8 to 12 from two different public schools in Heredia (Laboratorio School and San Isidro School) participated in this study. The instrument used was a Multiple Intelligences Test for school age children (Vega, 2006), which consists of 15 items subdivided in seven categories: linguistic, logical-mathematical, visual, kinaesthetic, musical, interpersonal, and intrapersonal. Descriptive and inferential statistics (Two-Way ANOVA) were used for the analysis of data.  Significant differences were found in linguistic intelligence (F:9.47; p < 0.01) between the MPC school (3.24±1.24 points) and the academicist school (2.31±1.10 points).  Differences were also found between sex (F:5.26; p< 0.05), for girls (3.25±1.02 points) and boys (2.52±1.30 points). In addition, the musical intelligence showed significant statistical differences between sexes (F: 7.97; p < 0.05).  In conclusion, the learning pedagogical models in Costa Rican public schools must be updated based on the new learning trends.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Rogue waves generation via nonlinear soliton collision in multiple-soliton state of a mode-locked fiber laser

We report for the first time, rogue waves generation in a mode-locked fiber laser that worked in multiple-soliton state in which hundreds of solitons occupied the whole laser cavity. Using real-time spatio-temporal intensity dynamics measurements, it is unveiled that nonlinear soliton collision accounts for the formation of rogue waves in this laser state. The nature of interactions between solitons are also discussed. Our observation may suggest similar formation mechanisms of rogue waves in other systems.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Molecular changes induced by a centrally-induced state of synthetic torpor in multiple organs:a new strategy for radioprotection.

Torpor is a successful survival strategy displayed by several mammalian species to cope with harsh environmental conditions. A complex interplay of ambient, genetic and circadian stimuli acts centrally to induce a severe suppression of metabolic rate, usually followed by an apparently undefended reduction of body temperature. Some animals, such as marmots, are able to maintain this physiological state for months (hibernation), during which torpor bouts are periodically interrupted by short interbouts of normothermia (arousals). Interestingly, torpor adaptations have been shown to be associated with a large resistance towards stressors, such as radiation: indeed, if irradiated during torpor, hibernators can tolerate higher doses of radiation, showing an increased survival rate. New insights for radiotherapy and long-term space exploration could arise from the induction of torpor in non-hibernators, like humans. The present research project is centered on synthetic torpor (ST), a hypometabolic/hypothermic condition induced in a non-hibernator, the rat, through the pharmacological inhibition of the Raphe Pallidus, a key brainstem area controlling thermogenic effectors. By exploiting this procedure, this thesis aimed at: i) providing a multiorgan description of the functional cellular adaptations to ST; ii) exploring the possibility, and the underpinning molecular mechanisms, of enhanced radioresistance induced by ST. To achieve these aims, transcriptional and histological analysis have been performed in multiple organs of synthetic torpid rats and normothermic rats, either exposed or not exposed to 3 Gy total body of X-rays. The results showed that: i) similarly to natural torpor, ST induction leads to the activation of survival and stress resistance responses, which allow the organs to successfully adapt to the new homeostasis; ii) ST provides tissue protection against radiation damage, probably mainly through the cellular adaptations constitutively induced by ST, even though the triggering of specific responses when the animal is irradiated during hypothermia might play a role.

Intermediate linguistic task fine-tuning on Multi-lingual models

State-of-the-art NLP systems are generally based on the assumption that the underlying models are provided with vast datasets to train on. However, especially when working in multi-lingual contexts, datasets are often scarce, thus more research should be carried out in this field. This thesis investigates the benefits of introducing an additional training step when fine-tuning NLP models, named Intermediate Training, which could be exploited to augment the data used for the training phase. The Intermediate Training step is applied by training models on NLP tasks that are not strictly related to the target task, aiming to verify if the models are able to leverage the learned knowledge of such tasks. Furthermore, in order to better analyze the synergies between different categories of NLP tasks, experimentations have been extended also to Multi-Task Training, in which the model is trained on multiple tasks at the same time.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

[diet Quality And Associated Factors Among The Elderly: A Population-based Study In Campinas, São Paulo State, Brazil].

The aim of this study was to assess the quality of diet among the elderly and associations with socio-demographic variables, health-related behaviors, and diseases. A population-based cross-sectional study was conducted in a representative sample of 1,509 elderly participants in a health survey in Campinas, São Paulo State, Brazil. Food quality was assessed using the Revised Diet Quality Index (DQI-R). Mean index scores were estimated and a multiple regression model was employed for the adjusted analyses. The highest diet quality scores were associated with age 80 years or older, Evangelical religion, diabetes mellitus, and physical activity, while the lowest scores were associated with home environments shared with three or more people, smoking, and consumption of soft drinks and alcoholic beverages. The findings emphasize a general need for diet quality improvements in the elderly, specifically in subgroups with unhealthy behaviors, who should be targeted with comprehensive strategies.

Ankhd1 Represses P21 (waf1/cip1) Promoter And Promotes Multiple Myeloma Cell Growth.

ANKHD1 (Ankyrin repeat and KH domain-containing protein 1) is highly expressed and plays an important role in the proliferation and cell cycle progression of multiple myeloma (MM) cells. ANKHD1 downregulation modulates cell cycle gene expression and upregulates p21 irrespective of the TP53 mutational status of MM cell lines. The present study was aimed to investigate the role of ANKHD1 in MM in vitro clonogenicity and in vivo tumourigenicity, as well as the role of ANKHD1 in p21 transcriptional regulation. ANKHD1 silencing in MM cells resulted in significantly low no. of colonies formed and in slow migration as compared to control cells (p < 0.05). Furthermore, in xenograft MM mice models, tumour growth was visibly suppressed in mice injected with ANKHD1 silenced cells compared to the control group. There was a significant decrease in tumour volume (p = 0.006) as well as in weight (p = 0.02) in the group injected with silenced cells compared to those of the control group. Co-immunoprecipitation and chromatin immunoprecipitation (ChIP) assays confirmed the interaction between p21 and ANKHD1. Moreover, overexpression of ANKHD1 downregulated the activity of a p21 promoter in luciferase assays. Decrease in luciferase activity suggests a direct role of ANKHD1 in p21 transcriptional regulation. In addition confocal analysis after U266 cells were treated with Leptomycin B (LMB) for 24 h showed accumulation of ANKHD1 inside the nucleus as compared to untreated cells where ANKHD1 was found to be predominantly in cytoplasm. This suggests ANKHD1 might be shuttling between cytoplasm and nucleus. In conclusion, ANKHD1 promotes MM growth by repressing p21 a potent cell cycle regulator.

Modelos de regressão lineares para estimativa de produtividade da soja no oeste do Paraná, utilizando dados espectrais

The main objective of this work was to evaluate the linear regression between spectral response and soybean yield in regional scale. In this study were monitored 36 municipalities from the west region of the states of Parana using five images of Landsat 5/TM during 2004/05 season. The spectral response was converted in physical values, apparent and surface reflectances, by radiometric transformation and atmospheric corrections and both used to calculate NDVI and GVI vegetation indices. Those ones were compared by multiple and simple regression with government official yield values (IBGE). Diagnostic processing method to identify influents values or collinearity was applied to the data too. The results showed that the mean surface reflectance value from all images was more correlated with yield than individual dates. Further, the multiple regressions using all dates and both vegetation indices gave better results than simple regression.

Oral health conditions among the elderly in Southeastern São Paulo State

OBJECTIVE: This study aimed to asses oral health conditions in a population aged 60 years and over living in Botucatu, Southeastern Brazil. A cross-sectional population-based study was carried out using a random sample (N=372) of the urban population aged 60 years and over from the city of Botucatu, in 2005. World Health Organization criteria and codes for oral health epidemiological surveys were used. Re-examination was carried out in 10% of individuals aiming to evaluate intra-examiner agreement. Statistical analysis was performed by one-way ANOVA or Kruskal-Wallis ANOVA, as applicable. Also, the t-test was used in the absence of homoscedasticity. Fisher's exact test was used for situations where the categories with less than five units were observed. Adjusted residuals and multiple-comparison analysis were conducted to identify associations between variable categories and subgroups. The intra-examiner agreement was 98% and Kappa statistics result was 0.95. Loss component represented 90.68% of DMF-T index, which was 29.85. The prevalence of edentulism was 63.17%. Upper and lower dentures were found in 80% and 58% respectively, with complete denture as the most commonly used. In those studied, 15% required upper and 38% lower dentures. There was more need for complete denture in both jaws. Approximately 20% had soft tissue alterations. For periodontal conditions, most sextants were excluded (81.81%). Periodontal pockets (4 - 5 mm) were seen in 11.29% of the examined individuals. The oral health status of the elderly population in Botucatu is poor, as well as in other Brazilian cities. The results of this study may help planning collective health actions, giving an accurate description of the oral problems among the elderly.

Effects of flooding of the River Paraná on the temporal activity of Anopheles (Nyssorhynchus) darlingi Root (Diptera: Culicidae), at the border state of Mato Grosso do Sul and São Paulo, Brazil

INTRODUCTION: Study of the temporal activity of malaria vectors during the implantation of a hydroelectric power station on the River Paraná, intended to generate electrical energy. The river separates the States of São Paulo and Mato Grosso do Sul, in Brazil. The objective was to verify whether alterations occurred in the wealth and diversity indices of Anopheles, following two successive floods, extended to the temporal activity and nycthemeral rhythm followed over a five year period. METHODS: Mosquito capture was performed monthly using the Human Attraction Technique and Shannon Traps. The first, executed for 24h, provided the nycthemeral rhythm and the second, lasting 15h, permitted the tracking of Anopheles during the two floods. RESULTS: The bimodal pattern of Anopheles darlingi defined before these floods was modified throughout the environment interventions. The same effect had repercussions on the populations of An albitarsis s.l., An triannulatus and An galvaoi. Activity prior to twilight was less affected by the environment alterations. CONCLUSIONS: The dam construction provoked changes in Anopheles temporal activity patterns, permitting classification of the area as an ecologically steady and unstable situation. Differences observed in Anopheles behavior due to the capture methods revealed the influence of solo and multiple attractiveness inside the populations studied.

Anemia em crianças de 6 a 59 meses e fatores associados no Município de Jordão, Estado do Acre, Brasil

Examinou-se a prevalência e fatores associados à anemia em estudo transversal com 429 crianças de 6 a 59 meses do Município de Jordão, Estado do Acre, Brasil. Modelos múltiplos de regressão de Poisson foram utilizados com seleção hierárquica das variáveis independentes. A anemia foi altamente prevalente (57,3%; IC95%: 52,5%-62,1%). Ter idade entre 6 e 23,9 meses [razão de prevalência - RP (IC95%): 1,40 (1,09-1,74)], morar na área rural [RP: 1,23 (1,04-1,44)], morar em domicílio com 5 a 14 crianças [RP: 1,23 (1,04-1,44)], ter mãe que fumou na gravidez [RP: 1,29 (1,09-1,53)], mãe anêmica [RP: 1,18 (1,00-1,39)] e apresentar déficit de altura para idade [RP: 1,19 (1,01-1,39)] foram fatores associados ao risco de anemia, e ter mãe que trabalha fora [RP: 0,78 (0,64-0,94)] foi fator de proteção. A anemia é um grave problema de saúde pública nesse município. Estratégias multissetoriais de combate à pobreza, aumento da cobertura e qualidade de serviços de assistência à saúde materno-infantil devem ser implementados.