Entrepreneurial formations in Latin American agriculture starting from rural property structures of production

Includes bibliography

The dynamic behavior of a cantilever beam coupled to a non-ideal unbalanced motor through numerical and experimental analysis

An excitation force that is not influenced by the system state is said to be an ideal energy source. In real situations, a direct and feedback coupling between the excitation source and the system must always exist at a certain level. This manifestation of the law of conservation of energy is known as the Sommerfeld effect. In the case of obtaining a mathematical model for such a system, additional equations are usually necessary to describe the vibration sources with limited power and its coupling with the mechanical system. In this work, a cantilever beam and a non-ideal DC motor fixed to its free end are analyzed. The motor has an unbalanced mass that provides excitation to the system which is proportional to the current applied to the motor. During the coast up operation of the motor, if the drive power is increased slowly, making the excitation frequency pass through the first natural frequency of the beam, the DC motor speed will remain the same until it suddenly jumps to a much higher value (simultaneously its amplitude jumps to a much lower value) upon exceeding a critical input power. It was found that the Sommerfeld effect depends on some system parameters and the motor operational procedures. These parameters are explored to avoid the resonance capture in the Sommerfeld effect. Numerical simulations and experimental tests are used to help gather insight of this dynamic behavior. (C) 2014 Elsevier Ltd. All rights reserved.

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

Predictive maintenance of a worm reducer with an unbalanced load

Among all predictive maintenance techniques the oil analysis and vibration analysis are the most important for monitoring some mechanical systems. The integration of these techniques has potential to improve industrial maintenance practices and provide a better economic gain for industries. To study the integration of these two techniques, a test rig was set up to obtain an extreme working condition for the worm reducer used in this paper. The test rig was composed by a motor connected to a reducer through a flexible coupling and with an unbalanced load. The analysis of the results carried out by using a sample of the oil recommended by the manufacturer in extreme conditions, and using liquid contaminant is presented. From the results it was observed that if there is an abnormal instantaneous load in a system, the subsequent vibration analysis may not perceive what occurred if there was no permanent damage, which is not the case with the lubricant analysis.

On nonlinear dynamics behavior and control of a new model of a magnetically levitated vibrating system, excited by an unbalanced DC motor of limited power supply

We analyze new results on a magnetically levitated body (a block including a magnet whose bottom pole is set in such a way as to repel the upper pole of a magnetic base) excited by a non-ideal energy source (an unbalanced electric motor of limited power supply). These new results are related to the jump phenomena and increase of power required of such sources near resonance are manifestations of a non-ideal system and they are referred as the Sommerfeld effect, which emulates an energy sink. In this work, we also discuss control strategies to be applied to this system, in resonance conditions, in order to decrease its vibration amplitude and avoiding this apparent energy sink.

Influence of the starting time of preslaughter feed withdrawal on welfare of pigs and pork quality

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Comparison of Induction Motor Starting Methods Being Powered by a Diesel-Generator Set

Starting induction motors on isolated or weak systems is a highly dynamic process that can cause motor and load damage as well as electrical network fluctuations. Mechanical damage is associated with the high starting current drawn by a ramping induction motor. In order to compensate the load increase, the voltage of the electrical system decreases. Different starting methods can be applied to the electrical system to reduce these and other starting method issues. The purpose of this thesis is to build accurate and usable simulation models that can aid the designer in making the choice of an appropriate motor starting method. The specific case addressed is the situation where a diesel-generator set is used as the electrical supplied source to the induction motor. The most commonly used starting methods equivalent models are simulated and compared to each other. The main contributions of this thesis is that motor dynamic impedance is continuously calculated and fed back to the generator model to simulate the coupling of the electrical system. The comparative analysis given by the simulations has shown reasonably similar characteristics to other comparative studies. The diesel-generator and induction motor simulations have shown good results, and can adequately demonstrate the dynamics for testing and comparing the starting methods. Further work is suggested to refine the equivalent impedance presented in this thesis.

A Model-Based Approach for Small-Signal Stability Assessment of Unbalanced Power Systems

In this paper, a modeling technique for small-signal stability assessment of unbalanced power systems is presented. Since power distribution systems are inherently unbalanced, due to its lines and loads characteristics, and the penetration of distributed generation into these systems is increasing nowadays, such a tool is needed in order to ensure a secure and reliable operation of these systems. The main contribution of this paper is the development of a phasor-based model for the study of dynamic phenomena in unbalanced power systems. Using an assumption on the net torque of the generator, it is possible to precisely define an equilibrium point for the phasor model of the system, thus enabling its linearization around this point, and, consequently, its eigenvalue/eigenvector analysis for small-signal stability assessment. The modeling technique presented here was compared to the dynamic behavior observed in ATP simulations and the results show that, for the generator and controller models used, the proposed modeling approach is adequate and yields reliable and precise results.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Comportamento elastodinamico di meccanismi in catena chiusa bilanciati: analisi numerica e sperimentale di un quadrilatero articolato

The study presented in this work deals with the investigation of the effects produced by two common techniques of static balancing on the dynamic performances of closed-chain linkages, taking into account the compliance of the mechanism components. The long-term goal of the research consists in determining an optimal balancing strategy for parallel spatial manipulators. The present contribution is a starting point and it focuses on the planar four-bar linkage, intended as the simplest example of closed-chain mechanism. The elastodynamic behaviour of an unbalanced four-bar linkage and two balanced ones, respectively obtained by mass and elastic balancing, is investigated by means of both numerical simulations and experimental tests. The purpose of this work is to obtain preliminary results, to be refined and broadened in future developments

Educational mismathc in Italian an European labour markets (starting from the UK)

The present doctoral thesis is structured as a collection of three essays. The first essay, “SOC(HE)-Italy: a classification for graduate occupations” presents the conceptual basis, the construction, the validation and the application to the Italian labour force of the occupational classification termed SOC(HE)-Italy. I have developed this classification under the supervision of Kate Purcell during my period as a visiting research student at the Warwick Institute for Emplyment Research. This classification links the constituent tasks and duties of a particular job to the relevant knowledge and skills imparted via Higher Education (HE). It is based onto the SOC(HE)2010, an occupational classification first proposed by Kate Purcell in 2013, but differently constructed. In the second essay “Assessing the incidence and wage effects of overeducation among Italian graduates using a new measure for educational requirements” I utilize this classification to build a valid and reliable measure for job requirements. The lack of an unbiased measure for this dimension constitutes one of the major constraints to achieve a generally accepted measurement of overeducation. Estimations of overeducation incidence and wage effects are run onto AlmaLaurea data from the survey on graduates career paths. I have written this essay and obtained these estimates benefiting of the help and guidance of Giovanni Guidetti and Giulio Pedrini. The third and last essay titled “Overeducation in the Italian labour market: clarifying the concepts and addressing the measurement error problem” addresses a number of theoretical issues concerning the concepts of educational mismatch and overeducation. Using Istat data from RCFL survey I run estimates of the ORU model for the whole Italian labour force. In my knowledge, this is the first time ever such model is estimated on such population. In addition, I adopt the new measure of overeducation based onto the SOC(HE)-Italy classification.

Immune reconstitution inflammatory syndrome in patients starting antiretroviral therapy for HIV infection: a systematic review and meta-analysis

In patients with HIV-1 infection who are starting combination antiretroviral therapy (ART), the incidence of immune reconstitution inflammatory syndrome (IRIS) is not well defined. We did a meta-analysis to establish the incidence and lethality of the syndrome in patients with a range of previously diagnosed opportunistic infections, and examined the relation between occurrence and the degree of immunodeficiency. Systematic review identified 54 cohort studies of 13 103 patients starting ART, of whom 1699 developed IRIS. We calculated pooled cumulative incidences with 95% credibility intervals (CrI) by Bayesian methods and did a random-effects metaregression to analyse the relation between CD4 cell count and incidence of IRIS. In patients with previously diagnosed AIDS-defining illnesses, IRIS developed in 37.7% (95% CrI 26.6-49.4) of those with cytomegalovirus retinitis, 19.5% (6.7-44.8) of those with cryptococcal meningitis, 15.7% (9.7-24.5) of those with tuberculosis, 16.7% (2.3-50.7) of those with progressive multifocal leukoencephalopathy, and 6.4% (1.2-24.7) of those with Kaposi's sarcoma, and 12.2% (6.8-19.6) of those with herpes zoster. 16.1% (11.1-22.9) of unselected patients starting ART developed any type of IRIS. 4.5% (2.1-8.6) of patients with any type of IRIS died, 3.2% (0.7-9.2) of those with tuberculosis-associated IRIS died, and 20.8% (5.0-52.7) of those with cryptococcal meningitis died. Metaregression analyses showed that the risk of IRIS is associated with CD4 cell count at the start of ART, with a high risk in patients with fewer than 50 cells per microL. Occurrence of IRIS might therefore be reduced by initiation of ART before immunodeficiency becomes advanced.

Early mortality and loss to follow-up in HIV-infected children starting antiretroviral therapy in Southern Africa

Many HIV-infected children in Southern Africa have been started on antiretroviral therapy (ART), but loss to follow up (LTFU) can be substantial. We analyzed mortality in children retained in care and in all children starting ART, taking LTFU into account.

Prognosis of patients with HIV-1 infection starting antiretroviral therapy in sub-Saharan Africa: a collaborative analysis of scale-up programmes

Background Prognostic models have been developed for patients infected with HIV-1 who start combination antiretroviral therapy (ART) in high-income countries, but not for patients in sub-Saharan Africa. We developed two prognostic models to estimate the probability of death in patients starting ART in sub-Saharan Africa. Methods We analysed data for adult patients who started ART in four scale-up programmes in Côte d'Ivoire, South Africa, and Malawi from 2004 to 2007. Patients lost to follow-up in the first year were excluded. We used Weibull survival models to construct two prognostic models: one with CD4 cell count, clinical stage, bodyweight, age, and sex (CD4 count model); and one that replaced CD4 cell count with total lymphocyte count and severity of anaemia (total lymphocyte and haemoglobin model), because CD4 cell count is not routinely measured in many African ART programmes. Death from all causes in the first year of ART was the primary outcome. Findings 912 (8·2%) of 11 153 patients died in the first year of ART. 822 patients were lost to follow-up and not included in the main analysis; 10 331 patients were analysed. Mortality was strongly associated with high baseline CD4 cell count (≥200 cells per μL vs <25; adjusted hazard ratio 0·21, 95% CI 0·17–0·27), WHO clinical stage (stages III–IV vs I–II; 3·45, 2·43–4·90), bodyweight (≥60 kg vs <45 kg; 0·23, 0·18–0·30), and anaemia status (none vs severe: 0·27, 0·20–0·36). Other independent risk factors for mortality were low total lymphocyte count, advanced age, and male sex. Probability of death at 1 year ranged from 0·9% (95% CI 0·6–1·4) to 52·5% (43·8–61·7) with the CD4 model, and from 0·9% (0·5–1·4) to 59·6% (48·2–71·4) with the total lymphocyte and haemoglobin model. Both models accurately predict early mortality in patients starting ART in sub-Saharan Africa compared with observed data. Interpretation Prognostic models should be used to counsel patients, plan health services, and predict outcomes for patients with HIV-1 infection in sub-Saharan Africa.

Treatment modification in human immunodeficiency virus-infected individuals starting combination antiretroviral therapy between 2005 and 2008

Adverse effects of combination antiretroviral therapy (CART) commonly result in treatment modification and poor adherence.