Determinants of export diversification and sophistication in sub-saharan Africa

This paper studies the political and economic factors that determine successful export diversification (ED) and export sophistication (ES) strategies in the Sub-Saharan African (SSA) countries and also the way in which successful ED and sophistication strategies contribute to explain the improving in some of the millennium development goals (MDG). We run separate regressions for the determinants of ES and ED, using disaggregated data of the 48 SSA countries, from 1960 to 2005. The results suggest that better governance is an important determinant for the success of diversification and sophistication strategies in SSA. In particular the level of corruption, transparency and accountability are important factors in limiting or promoting the scope of diversification and the level of sophistication of the exports. The results also suggest that increases in human capital in SSA countries promote both ED and ES, showing that the level of education of the workforce is positively related with ES and ED, with higher levels of education (tertiary) playing a more important role in explaining ES, while lower levels of education (primary) being more important as determinants of ED. In the second part we explore the links between ED and ES and growth presenting evidence that ED and ES are linked to growth stability in SSA. This study also suggests that the Sub-Saharan countries that were more successful in achieving ED and ES tend to be more successful in improving the living conditions of their population. Using different variables of Infant Mortality (one of the MDG) and life expectancy as dependent variables, we present evidence that suggests that in SSA higher ED and ES are associated with lower infant mortality and higher life expectancy. We show that this result is robust, presenting positive and significant results even when a large number of different control variables are introduced, or when fixed effects and instrumental variables are considered. The evidence suggests that ED and ES are part of the solution for a successful development of SSA.

High or low initial setting price?: Initial setting price as prediction of intention to bid in on line auctions

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Seasonal variation of Lutzomyia longipalpis in Belo Horizonte, State of Minas Gerais

Between October, 1997 and September, 1999 in Belo Horizonte, Minas Gerais a study of seasonal variation of Lutzomyia longipalpis was carried out in three distinct areas of the municipality. Sand flies were sampled at 15-day intervals in three residences, in each of which two CDC light traps were installed, one indoors and the other in the peridomicile. A total of 397 sand flies were captured in the three areas, with 65%, 30% and 1% of specimens collected in the eastern, northeast and Barreiro districts, respectively. The overall proportions of sand flies collected inside and around the houses were similar (57% vs 43%) and this pattern was seen for both Lutzomyia longipalpis and Lutzomyia whitmani . The highest population levels during the two years of the study were from October to March. From October onwards, numbers increased constantly until February. A gradual fall was seen from April onwards until the lowest levels were reached in the months of June, July and August.

Seasonal profile and level of CD4+ lymphocytes in the occurrence of cryptosporidiosis and cystoisosporidiosis in HIV/AIDS patients in the Triângulo Mineiro region, Brazil

Patients with AIDS are particularly susceptible to infection with intestinal coccidia. In this study the prevalence of infections with Cryptosporidium sp and Cystoisospora belli were evaluated among HIV/AIDS patients in the Triângulo Mineiro region, Brazil. Between July 1993 and June 2003 faecal samples from 359 patients were collected and stained by a modified Ziehl-Neelsen method, resulting in 19.7% of positivity for coccidian (8.6% with Cryptosporidium sp, 10.3% with Cystoisospora belli and 0.8% with both coccidian). Patients with diarrhoea and T CD4+ lymphocyte levels < 200 cells/mm3 presented higher frequency of these protozoans, demonstrating the opportunistic profile of these infections and its relationship with the immunological status of the individual. It was not possible to determine the influence of HAART, since only 8.5% of the patients positive for coccidian received this therapy regularly. Parasitism by Cryptosporidium sp was more frequent between December and February and thus was characterised by a seasonal pattern of infection, which was not observed with Cystoisospora belli.

Numerical prediction of diffusion and electric field-induced iron nanoparticle transport

Zero valent iron nanoparticles (nZVI) are considered very promising for the remediation of contaminated soils and groundwaters. However, an important issue related to their limited mobility remains unsolved. Direct current can be used to enhance the nanoparticles transport, based on the same principles of electrokinetic remediation. In this work, a generalized physicochemical model was developed and solved numerically to describe the nZVI transport through porous media under electric field, and with different electrolytes (with different ionic strengths). The model consists of the Nernst–Planck coupled system of equations, which accounts for the mass balance of ionic species in a fluid medium, when both the diffusion and electromigration of the ions are considered. The diffusion and electrophoretic transport of the negatively charged nZVI particles were also considered in the system. The contribution of electroosmotic flow to the overall mass transport was included in the model for all cases. The nZVI effective mobility values in the porous medium are very low (10−7–10−4 cm2 V−1 s−1), due to the counterbalance between the positive electroosmotic flow and the electrophoretic transport of the negatively charged nanoparticles. The higher the nZVI concentration is in the matrix, the higher the aggregation; therefore, low concentration of nZVI suspensions must be used for successful field application.

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Credit default swap (CDS) prediction model & trading strategy

This project focuses on the study of different explanatory models for the behavior of CDS security, such as Fixed-Effect Model, GLS Random-Effect Model, Pooled OLS and Quantile Regression Model. After determining the best fitness model, trading strategies with long and short positions in CDS have been developed. Due to some specifications of CDS, I conclude that the quantile regression is the most efficient model to estimate the data. The P&L and Sharpe Ratio of the strategy are analyzed using a backtesting analogy, where I conclude that, mainly for non-financial companies, the model allows traders to take advantage of and profit from arbitrages.

Prediction of hyperglycemia in preterm newborn infants

Many conditions are associated with hyperglycemia in preterm neonates because they are very susceptible to changes in carbohydrate homeostasis. The purpose of this study was to evaluate the occurrence of hyperglycemia in preterm infants undergoing glucose infusion during the first week of life, and to enumerate the main variables predictive of hyperglycemia. This prospective study (during 1994) included 40 preterm neonates (gestational age <37 weeks); 511 determinations of glycemic status were made in these infants (average 12.8/infant), classified by gestational age, birth weight, glucose infusion rate and clinical status at the time of determination (based on clinical and laboratory parameters). The clinical status was classified as stable or unstable, as an indication of the stability or instability of the mechanisms governing glucose homeostasis at the time of determination of blood glucose; 59 episodes of hyperglycemia (11.5%) were identified. A case-control study was used (case = hyperglycemia; control = normoglycemia) to derive a model for predicting glycemia. The risk factors considered were gestational age (<=31 vs. >31 weeks), birth weight (<=1500 vs. >1500 g), glucose infusion rate (<=6 vs. >6 mg/kg/min) and clinical status (stable vs. unstable). Multivariate analysis by logistic regression gave the following mathematical model for predicting the probability of hyperglycemia: 1/exp{-3.1437 + 0.5819(GA) + 0.9234(GIR) + 1.0978(Clinical status)} The main predictive variables in our study, in increasing order of importance, were gestational age, glucose infusion rate and, the clinical status (stable or unstable) of the preterm newborn infant. The probability of hyperglycemia ranged from 4.1% to 36.9%.

THE VEGETATION OF A SEASONAL VÁRZEA FOREST IN THE LOWER SOLIMÕES RIVER, BRAZILIAN AMAZONIA

The species composition of the seasonal várzea forest growing on a bank of the Ilha de Marchantaria / lower Solimões-Amazonas River, Brazil was studied in an area of slightly less than one hectare. Two biomass plots were harvested. Forty-seven arboreal species representing 46 genera in 25 families were recorded. Tree density was 1086 per hectare. Total basal area was 45 m2 ha1. Mean species density was 6.5 ± 1.98 per 100 m2. The most abundant species were Crataeva benthamii(Capparidaceae), Laetia corymbutosa(Flacourtiaceae) and Vitex cymosa(Verbenaceae). The highest basal area per species was 10.2 m2 for Pseudobombax munguba(Bombacaceae). The common species are known to be typical floristic elements of the seasonal varzea forest. Above ground dry biomass was equal to 97 and 255 t ha', respectively. Its chemical composition is characterized by comparatively high bioelement contents equal to 2.4 percent on the average. Calcium was the most important bioelement. Structure of the forest and age darings of trees allow the successional classification of the stands.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

Integration of geometric consistency contributory factors in three-leg junctions collision prediction models of Portuguese two-lane national highways

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Seasonal changes of leaf Nitrogen content in trees of Amazonian floodplains

In Amazonian floodplains the trees are exposed to extreme flooding of up to 230 days a year. Waterlogging of the roots and stems affects growth and metabolic activity of the trees. An increased leaf fall in the aquatic period and annual increment rings in the wood indicate periodical growth reductions. The present study aims at documenting seasonal changes of metabolism and vitality of adult trees in the annual cycle as expressed by changes of leaf nitrogen content. Leaves of six tree species common in floodplains in Central Amazonia and typical representants of different growth strategies were collected every month between May 1994 and June 1995 in the vicinity of Manaus, Brazil. Mean leaf nitrogen content varied between 1.3% and 3.2% in the non-flooded trees. Three species showed significantly lower Ν content in the flooded period (p=0.05, 0.001, 0.001), the difference ranging 20-25% lower than in the non-flooded period. Two species showed no significant difference while Nectandra amazonum showed 32% more Ν in the flooded season (p=0.001). Leaf nitrogen content was generally high when new leaves were flushed (in the flooded period) and decreased continuously thereafter in all species. Three species showed an additional peak of nitrogen during the first month of the terrestrial phase, in leaves which had flushed earlier, indicating that flooding may disturb nitrogen uptake.

Dinâmica do sub-bosque e do estado arbóreo de floresta tropical primária fragmentada na Amazônia Oriental1

Objetivou-se neste trabalho avaliar a dinâmica florestal, sobretudo da regeneração natural, em um fragmento de floresta tropical primária, entre 1998 c 1999, em Peixe-Boi (PA). Foram demarcadas três parcelas permanentes (1 ha cada) onde todos os indivíduos com DAP ≥ 10 cm foram registrados; os indivíduos com 10cm ≥ DAP ≥ 5cm foram amostrados em 6.000 m2, aqueles entre 5cm ≥ DAP ≥ 2cm em 2.400 m2 e com DAP ≤ 2cm em 240 m2. Foram estimados 143.000 indivíduos, desde plântulas até árvores pertencentes a 337 espécies e 76 famílias. Mimosaceae foi a família de maior riqueza (44 espécies); 14 famílias ocorreram com uma única espécie sendo que metade delas apresentaram também um único indivíduo. Independentemente da classe diamétrica verificou-se o egresso de 56 espécies versus o ingresso de 68, gerando um ganho líquido de 12 espécies. A dinâmica da composição e da abundância da regeneração natural foi muito intensa. Observou-se a saída de uma família face ao ingresso de outras 14, aumentando cm quase 30% o número de espécies. A maior mortalidade foi verificada em Bauhinia cf. rutilans e Mabea aff. speciosa (300 e 21 indivíduos). Rinorea negleta e Leçythis idatimon recrutaram 171 e 89 espécimes. A razão recrutamento/ mortalidade foi, em quaisquer das classes diamétricas, sempre superior a unidade. O estoque de mudas para se obter uma árvore, uma arvoreta e uma vara foi, respectivamente de 297, 160 e 48 mudas. O número de espécies e a abundância aumentaram no período, assim como a área basal e a biomassa.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.