Lujien painelaiteterästen hitsaus

Työn tavoitteena oli tutkia lujan nuorrutetun painelaiteteräksen P500QL2 hitsattavuutta ja koehitsausten avulla löytää optimaaliset hitsausparametrit ja lämmöntuonti teräksen hitsaukseen. Työn tavoitteena oli myös selvittää ja käsitellä kaikkien painelaiteterässtandardissa esitettyjen lujien painelaiteterästen hitsauksessa huomioon otettavia asioita. Työn teoriaosuudessa käsitellään lujien painelaiteterästen hitsauksessa huomioitavia erityispiirteitä, kuten lämmöntuontia, jäähtymisaikaa, esilämmitystä sekä hitsausaineiden valintaa. Lisäksi teoriaosuudessa käsitellään painelaitteiden valmistusta, painelaiteterässtandardiin kuuluvia lujia painelaiteteräksiä sekä keinoja lujien terästen hitsattavuuden arviointiin. Työn kokeellisessa osassa tutkittiin aineenvahvuudeltaan 50 mm paksun P500QL2-teräksen päittäisliitoksen mekaanisia ominaisuuksia eri lämmöntuonneilla hitsattuna. Kokeellisessa osassa tutkittiin myös myöstön poisjättämisen vaikutuksia liitoksen mekaanisiin ominaisuuksiin. Mekaanisia ominaisuuksia tutkittiin toteuttamalla koekappaleiden aineenkoetus menetelmäkoestandardin vaatimuksia soveltaen. Tutkimuksessa käytettyjä testausmenetelmiä olivat silmämääräinen tarkastus, magneettijauhetarkastus, ultraäänitarkastus, mikro- ja makrorakennetarkastelu, kovuuskokeet, vetokokeeet ja iskukokeet. Testauksessa saatujen tulosten avulla lujan painelaiteteräksen P500QL2 hitsaukseen laadittiin alustava hitsausohje. Hitsausliitosten testauksessa saatujen tulosten perusteella havaittiin hitsien lujuuden ja kovuuden laskevan lämmöntuonnin kasvaessa. Hitsausliitosten iskusitkeysominaisuudet olivat erinomaiset vielä suurellakin lämmöntuonnilla, mutta liitosten murtovenymäarvot laskivat lämmöntuonnin kasvaessa. Myöstön havaittiin parantavan hitsin mekaanisia ominaisuuksia huomattavasti. Tutkimuksen tulosten perusteella painelaiteteräs P500QL2 on hitsattavissa suurella lämmöntuonnilla ja suurella tuottavuudella liitoksen täyttäessä painelaitevalmistuksen edellyttämät vaatimukset.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Nutrient cycling disturbance in Atlantic Forest sites affected by air pollution coming from the industrial complex of Cubatão, Southeast Brazil

Several aspects of nutrient cycling were studied at two sites of Atlantic Forest, in São Paulo State, Southeast Brazil (23o46’ S; 46o18’ W), which exhibited different degrees of forest structure decline caused by the air pollution emitted by the industrial complex of Cubatão, being referred here as the most and least affected sites (MAS and LAS, respectively). These investigations were developed during 1984 - 1986, a period in which the most severe negative effects of air pollution could be observed. Concentrations and amounts of N, P, K, Ca, Mg and S in four ecosystem compartments (leaves, litter layer, soil and roots) and in rainfall, throughfall and litterfall are briefly presented. At each site, the content of mineral elements generally decreased from leaves to litterfall and litter layer on the forest floor. Soil surface layer (0 - 5 cm) in both sites was the richest in mineral elements. Soil fertility was greater at LAS. In general, nutrient amounts remaining in the compartments and cycling through the ecosystem were greater at LAS as well, which could be due to the higher complexity of the forest structure at this site. Rainfall contributed more to soil inputs of K, Ca, Mg and S than litterfall at both sites. The nutrient residence times in the litter layer were higher and the index of nutrient use efficiency was lower at the most affected site. It was concluded that nutrient cycling was disturbed by air pollution at both sites, but to a greater extent at MAS. The main consequences of the air pollution stress were detected in the flux of nutrients through litterfall and in the litter layer on the forest floor.

Scheduling dynamic dataflow graphs with model checking

With the shift towards many-core computer architectures, dataflow programming has been proposed as one potential solution for producing software that scales to a varying number of processor cores. Programming for parallel architectures is considered difficult as the current popular programming languages are inherently sequential and introducing parallelism is typically up to the programmer. Dataflow, however, is inherently parallel, describing an application as a directed graph, where nodes represent calculations and edges represent a data dependency in form of a queue. These queues are the only allowed communication between the nodes, making the dependencies between the nodes explicit and thereby also the parallelism. Once a node have the su cient inputs available, the node can, independently of any other node, perform calculations, consume inputs, and produce outputs. Data ow models have existed for several decades and have become popular for describing signal processing applications as the graph representation is a very natural representation within this eld. Digital lters are typically described with boxes and arrows also in textbooks. Data ow is also becoming more interesting in other domains, and in principle, any application working on an information stream ts the dataflow paradigm. Such applications are, among others, network protocols, cryptography, and multimedia applications. As an example, the MPEG group standardized a dataflow language called RVC-CAL to be use within reconfigurable video coding. Describing a video coder as a data ow network instead of with conventional programming languages, makes the coder more readable as it describes how the video dataflows through the different coding tools. While dataflow provides an intuitive representation for many applications, it also introduces some new problems that need to be solved in order for data ow to be more widely used. The explicit parallelism of a dataflow program is descriptive and enables an improved utilization of available processing units, however, the independent nodes also implies that some kind of scheduling is required. The need for efficient scheduling becomes even more evident when the number of nodes is larger than the number of processing units and several nodes are running concurrently on one processor core. There exist several data ow models of computation, with different trade-offs between expressiveness and analyzability. These vary from rather restricted but statically schedulable, with minimal scheduling overhead, to dynamic where each ring requires a ring rule to evaluated. The model used in this work, namely RVC-CAL, is a very expressive language, and in the general case it requires dynamic scheduling, however, the strong encapsulation of dataflow nodes enables analysis and the scheduling overhead can be reduced by using quasi-static, or piecewise static, scheduling techniques. The scheduling problem is concerned with nding the few scheduling decisions that must be run-time, while most decisions are pre-calculated. The result is then an, as small as possible, set of static schedules that are dynamically scheduled. To identify these dynamic decisions and to find the concrete schedules, this thesis shows how quasi-static scheduling can be represented as a model checking problem. This involves identifying the relevant information to generate a minimal but complete model to be used for model checking. The model must describe everything that may affect scheduling of the application while omitting everything else in order to avoid state space explosion. This kind of simplification is necessary to make the state space analysis feasible. For the model checker to nd the actual schedules, a set of scheduling strategies are de ned which are able to produce quasi-static schedulers for a wide range of applications. The results of this work show that actor composition with quasi-static scheduling can be used to transform data ow programs to t many different computer architecture with different type and number of cores. This in turn, enables dataflow to provide a more platform independent representation as one application can be fitted to a specific processor architecture without changing the actual program representation. Instead, the program representation is in the context of design space exploration optimized by the development tools to fit the target platform. This work focuses on representing the dataflow scheduling problem as a model checking problem and is implemented as part of a compiler infrastructure. The thesis also presents experimental results as evidence of the usefulness of the approach.

Neural reflex regulation of arterial pressure in pathophysiological conditions: interplay among the baroreflex, the cardiopulmonary reflexes and the chemoreflex

The maintenance of arterial pressure at levels adequate to perfuse the tissues is a basic requirement for the constancy of the internal environment and survival. The objective of the present review was to provide information about the basic reflex mechanisms that are responsible for the moment-to-moment regulation of the cardiovascular system. We demonstrate that this control is largely provided by the action of arterial and non-arterial reflexes that detect and correct changes in arterial pressure (baroreflex), blood volume or chemical composition (mechano- and chemosensitive cardiopulmonary reflexes), and changes in blood-gas composition (chemoreceptor reflex). The importance of the integration of these cardiovascular reflexes is well understood and it is clear that processing mainly occurs in the nucleus tractus solitarii, although the mechanism is poorly understood. There are several indications that the interactions of baroreflex, chemoreflex and Bezold-Jarisch reflex inputs, and the central nervous system control the activity of autonomic preganglionic neurons through parallel afferent and efferent pathways to achieve cardiovascular homeostasis. It is surprising that so little appears in the literature about the integration of these neural reflexes in cardiovascular function. Thus, our purpose was to review the interplay between peripheral neural reflex mechanisms of arterial blood pressure and blood volume regulation in physiological and pathophysiological states. Special emphasis is placed on the experimental model of arterial hypertension induced by N-nitro-L-arginine methyl ester (L-NAME) in which the interplay of these three reflexes is demonstrable

Sucrose ingestion causes opioid analgesia

The intake of saccharin solutions for relatively long periods of time causes analgesia in rats, as measured in the hot-plate test, an experimental procedure involving supraspinal components. In order to investigate the effects of sweet substance intake on pain modulation using a different model, male albino Wistar rats weighing 180-200 g received either tap water or sucrose solutions (250 g/l) for 1 day or 14 days as their only source of liquid. Each rat consumed an average of 15.6 g sucrose/day. Their tail withdrawal latencies in the tail-flick test (probably a spinal reflex) were measured immediately before and after this treatment. An analgesia index was calculated from the withdrawal latencies before and after treatment. The indexes (mean ± SEM, N = 12) for the groups receiving tap water for 1 day or 14 days, and sucrose solution for 1 day or 14 days were 0.09 ± 0.04, 0.10 ± 0.05, 0.15 ± 0.08 and 0.49 ± 0.07, respectively. One-way ANOVA indicated a significant difference (F(3,47) = 9.521, P<0.001) and the Tukey multiple comparison test (P<0.05) showed that the analgesia index of the 14-day sucrose-treated animals differed from all other groups. Naloxone-treated rats (N = 7) receiving sucrose exhibited an analgesia index of 0.20 ± 0.10 while rats receiving only sucrose (N = 7) had an index of 0.68 ± 0.11 (t = 0.254, 10 degrees of freedom, P<0.03). This result indicates that the analgesic effect of sucrose depends on the time during which the solution is consumed and extends the analgesic effects of sweet substance intake, such as saccharin, to a model other than the hot-plate test, with similar results. Endogenous opioids may be involved in the central regulation of the sweet substance-produced analgesia.

Green supplier selection via Multiple Criteria Data Envelopment Analysis

An appropriate supplier selection and its profound effects on increasing the competitive advantage of companies has been widely discussed in supply chain management (SCM) literature. By raising environmental awareness among companies and industries they attach more importance to sustainable and green activities in selection procedures of raw material providers. The current thesis benefits from data envelopment analysis (DEA) technique to evaluate the relative efficiency of suppliers in the presence of carbon dioxide (CO2) emission for green supplier selection. We incorporate the pollution of suppliers as an undesirable output into DEA. However, to do so, two conventional DEA model problems arise: the lack of the discrimination power among decision making units (DMUs) and flexibility of the inputs and outputs weights. To overcome these limitations, we use multiple criteria DEA (MCDEA) as one alternative. By applying MCDEA the number of suppliers which are identified as efficient will be decreased and will lead to a better ranking and selection of the suppliers. Besides, in order to compare the performance of the suppliers with an ideal supplier, a “virtual” best practice supplier is introduced. The presence of the ideal virtual supplier will also increase the discrimination power of the model for a better ranking of the suppliers. Therefore, a new MCDEA model is proposed to simultaneously handle undesirable outputs and virtual DMU. The developed model is applied for green supplier selection problem. A numerical example illustrates the applicability of the proposed model.

The brain decade in debate: VIII. Peptide hormones and behavior: cholecystokinin and prolactin

This article is a transcription of an electronic symposium held on November 28, 2000 in which active researchers were invited by the Brazilian Society of Neuroscience and Behavior (SBNeC) to discuss the advances of the last decade in the peptide field with particular focus on central actions of prolactin and cholecystokinin. The comments in this symposium reflect the diversity of prolactin and cholecystokinin research and demonstrate how the field has matured. Since both peptides play a role in reproductive behaviors, particularly mother-infant interactions, this was the starting point of the discussion. Recent findings on the role of the receptor subtypes as well as interaction with other peptides in this context were also discussed. Another issue discussed was the possible role of these peptides in dopamine-mediated rewarding systems. Both prolactin and cholecystokinin are involved in mechanisms controlling food intake and somatic pain thresholds. The role of peripheral inputs through vagal afferents modulating behavior was stressed. The advent of knockout animals as potential generators of new knowledge in this field was also addressed. Finally, interactions with other neuropeptides and investigation of the role of these peptides in other fields such as immunology were mentioned. Knowledge about the central functions of prolactin and cholecystokinin has shown important advances. The role of these peptides in neurological and psychiatric syndromes such as anorexia, drug abuse and physiological disturbances that lead to a compromised maternal behavior seems relevant.

Angiotensin and baroreflex control of the circulation

There is a close association between the location of angiotensin (Ang) receptors and many important brain nuclei involved in the regulation of the cardiovascular system. The present review encompasses the physiological role of Ang II in the brainstem, particularly in relation to its influence on baroreflex control of the heart and kidney. Activation of AT1 receptors in the brainstem by fourth ventricle (4V) administration to conscious rabbits or local administration of Ang II into the rostral ventrolateral medulla (RVLM) of anesthetized rabbits acutely increases renal sympathetic nerve activity (RSNA) and RSNA baroreflex responses. Administration of the Ang antagonist Sarile into the RVLM of anesthetized rabbits blocked the effects of Ang II on the RSNA baroreflex, indicating that the RVLM is the major site of sympathoexcitatory action of Ang II given into the cerebrospinal fluid surrounding the brainstem. However, in conscious animals, blockade of endogenous Ang receptors in the brainstem by the 4V AT1 receptor antagonist losartan resulted in sympathoexcitation, suggesting an overall greater activity of endogenous Ang II within the sympathoinhibitory pathways. However, the RSNA response to airjet stress in conscious rabbits was markedly attenuated. While we found no effect of acute central Ang on heart rate baroreflexes, chronic 4V infusion inhibited the baroreflex and chronic losartan increased baroreflex gain. Thus, brainstem Ang II acutely alters sympathetic responses to specific afferent inputs thus forming part of a potentially important mechanism for the integration of autonomic response patterns. The sympathoexcitatory AT1 receptors appear to be activated during stress, surgery and anesthesia.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Spatial and temporal variation in fish populations and assemblages in coastal waters of the northern Baltic Proper

The distribution and traits of fish are of interest both ecologically and socio-economically. In this thesis, phenotypic and structural variation in fish populations and assemblages was studied on multiple spatial and temporal scales in shallow coastal areas in the archipelago of the northern Baltic Proper. In Lumparn basin in Åland Islands, the fish assemblage displayed significant seasonal variation in depth zone distribution. The results indicate that investigating both spatial and temporal variation in small scale is crucial for understanding patterns in fish distribution and community structure in large scale. The local population of Eurasian perch Perca fluviatilis L displayed habitat-specific morphological and dietary variation. Perch in the pelagic zone were on average deeper in their body shape than the littoral ones and fed on fish and benthic invertebrates. The results differ from previous studies conducted in freshwater habitats, where the pelagic perch typically are streamlined in body shape and zooplanktivorous. Stable isotopes of carbon and nitrogen differed between perch with different stomach contents, suggesting differentiation of individual diet preferences. In the study areas Lumparn and Ivarskärsfjärden in Åland Islands and Galtfjärden in Swedish east coast, the development in fish assemblages during the 2000’s indicated a general shift towards higher abundances of small-bodied lower-order consumers, especially cyprinids. For European pikeperch Sander lucioperca L., recent declines in adult fish abundances and high mortalities (Z = 1.06–1.16) were observed, which suggests unsustainably high fishing pressure on pikeperch. Based on the results it can be hypothesized that fishing has reduced the abundances of large predatory fish, which together with bottom-up forcing by eutrophication has allowed the lower-order consumer species to increase in abundances. This thesis contributes to the scientific understanding of aquatic ecosystems with new descriptions on morphological and dietary adaptations in perch in brackish water, and on the seasonal variation in small-scale spatial fish distribution. The results also demonstrate anthropogenic effects on coastal fish communities and underline the urgency of further reducing nutrient inputs and regulating fisheries in the Baltic Sea region.

The immunomodulator glatiramer acetate influences spinal motoneuron plasticity during the course of multiple sclerosis in an animal model

The immunomodulador glatiramer acetate (GA) has been shown to significantly reduce the severity of symptoms during the course of multiple sclerosis and in its animal model - experimental autoimmune encephalomyelitis (EAE). Since GA may influence the response of non-neuronal cells in the spinal cord, it is possible that, to some extent, this drug affects the synaptic changes induced during the exacerbation of EAE. In the present study, we investigated whether GA has a positive influence on the loss of inputs to the motoneurons during the course of EAE in rats. Lewis rats were subjected to EAE associated with GA or placebo treatment. The animals were sacrificed after 15 days of treatment and the spinal cords processed for immunohistochemical analysis and transmission electron microscopy. A correlation between the synaptic changes and glial activation was obtained by performing labeling of synaptophysin and glial fibrillary acidic protein using immunohistochemical analysis. Ultrastructural analysis of the terminals apposed to alpha motoneurons was also performed by electron transmission microscopy. Interestingly, although the GA treatment preserved synaptophysin labeling, it did not significantly reduce the glial reaction, indicating that inflammatory activity was still present. Also, ultrastructural analysis showed that GA treatment significantly prevented retraction of both F and S type terminals compared to placebo. The present results indicate that the immunomodulator GA has an influence on the stability of nerve terminals in the spinal cord, which in turn may contribute to its neuroprotective effects during the course of multiple sclerosis.

Method for automatic detection of wheezing in lung sounds

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients.

Participation of neuronal nitric oxide synthase in experimental neuropathic pain induced by sciatic nerve transection

Nerve injury leads to a neuropathic pain state that results from central sensitization. This phenomenom is mediated by NMDA receptors and may involve the production of nitric oxide (NO). In this study, we investigated the expression of the neuronal isoform of NO synthase (nNOS) in the spinal cord of 3-month-old male, Wistar rats after sciatic nerve transection (SNT). Our attention was focused on the dorsal part of L3-L5 segments receiving sensory inputs from the sciatic nerve. SNT resulted in the development of neuropathic pain symptoms confirmed by evaluating mechanical hyperalgesia (Randall and Selitto test) and allodynia (von Frey hair test). Control animals did not present any alteration (sham-animals). The selective inhibitor of nNOS, 7-nitroindazole (0.2 and 2 µg in 50 µL), blocked hyperalgesia and allodynia induced by SNT. Immunohistochemical analysis showed that nNOS was increased (48% by day 30) in the lumbar spinal cord after SNT. This increase was observed near the central canal (Rexed’s lamina X) and also in lamina I-IV of the dorsal horn. Real-time PCR results indicated an increase of nNOS mRNA detected from 1 to 30 days after SNT, with the highest increase observed 1 day after injury (1469%). Immunoblotting confirmed the increase of nNOS in the spinal cord between 1 and 15 days post-lesion (20%), reaching the greatest increase (60%) 30 days after surgery. The present findings demonstrate an increase of nNOS after peripheral nerve injury that may contribute to the increase of NO production observed after peripheral neuropathy.