Infraestructura de comunicaciones para algoritmos colaborativos

Este proyecto intenta crear un sistema cliente/servidor de comunicaciones para distintas máquinas paralelas en C++ utilizando WCF como tecnología en el servidor.

Health effects of occupational exposure to traffic particles and noise

Exposure to fine particles and noise has been linked to cardiovascular diseases and elevated cardiovascular mortality affecting the worldwide population. Residence and/or work in proximity to emission sources as for example road traffic leads to an elevated exposure and a higher risk for adverse health effects. Highway maintenance workers spend most of their work time in traffic and are exposed regularly to particles and noise. The aims of this thesis were to provide a better understanding of the workers' mixed exposure to particles and noise and to assess cardiopulmonary short term health effects in relation to this exposure. Exposure and health data were collected in collaboration with 8 maintenance centers of the Swiss Road Maintenance Services located in the cantons Bern, Fribourg and Vaud in western Switzerland. Repeated measurements with 18 subjects were conducted during 50 non-consecutive work shifts between Mai 2010 and February 2012, equally distributed over all seasons. In the first part of this thesis we tested and validated measurements of ultrafine particles with a miniature diffusion size classifier (miniDiSC) - a novel particle counting device that was used for the exposure assessment during highway maintenance work. We found that particle numbers and average particle size measured by the miniDiSC were highly correlated with data from the P-TRAK, a condensation particle counter (CPC), as well as from a scanning mobility particle sizer (SMPS). However, the miniDiSC measured significantly more particles than the P-TRAK and significantly less than the SMPS in its full size range. Our data suggests that the instrument specific cutoffs were the main reason for the different particle counts. The first main objective of this thesis was to investigate the exposure of highway maintenance workers to air pollutants and noise, in relation to the different maintenance activities. We have seen that the workers are regularly exposed to high particle and noise levels. This was a consequence of close proximity to highway traffic and the use of motorized working equipment such as brush cutters, chain saws, generators and pneumatic hammers during which the highest exposure levels occurred. Although exposure to air pollutants were not critical if compared to occupational exposure limits, the elevated exposure to particles and noise may lead to a higher risk for cardiovascular diseases in this worker population. The second main objective was to investigate cardiopulmonary short-term health effects in relation to the particle and noise exposure during highway maintenance work. We observed a PM2.5 related increase of the acute-phase inflammation markers C-reactive protein and serum amyloid A and a decrease of TNFa. Heart rate variability increased as a consequence of particle as well as noise exposure. Increased high frequency power indicated a stronger parasympathetic influence on the heart. Elevated noise levels during recreational time, after work, were related to increased blood pressure. Our data confirmed that highway maintenance workers are exposed to elevated levels of particles and noise as compared to the average population. This exposure poses a cardiovascular health risk and it is therefore important to make efforts to better protect the workers health. The use of cleaner machines during maintenance work would be a major step to improve the workers' situation. Furthermore, regulatory policies with the aim of reducing combustion and non-combustion emissions from road traffic are important for the protection of workers in traffic environments and the entire population.

Whole-Body Vibration Training Elevates Creatine-Kinase Levels in Sedentary Subjects

METHODS: 20 inactive (10 male, 10 female) underwent a single typical WBV session, with a total of 27 minutes of exercise on an oscillating platform at 26 Hz, involving upper and lower body muscles. Each exercise lasted 90 seconds, with 40 seconds pauses inbetween. Muscle enzymes (CK, transaminase, LDH, troponin I) were measured before, at 24, 48 and 96 hours post exercise. Lactate was measured immediately after the session. Muscle aches were assessed during 4 days post-exercise.RESULTS: Subjects' mean age was 23.0 ± 3.5 (male), 22.4 ± 1.4 (female), BMI 22.8 ± 2.3 and 22.1 ± 1.9, and all had been inactive for at least 12 months. Post exercise lactatemia was 10.0 ± 2.4 and 6.9 ± 2.4. CK elevation was significant (at least doubling of baseline values) in 1 male and 4 female subjects, while they remained at baseline values for the remaining 15 subjects. One female subject peaked at 3520 U/l at 96 hours post exercise, and all but one peaked at the same late time. Troponin and CK-MB never increased. No correlation was found between muscle soreness and CK levels.CONCLUSIONS: WBV can elicit important anaerobic processes reflected by the high lactacidemia, and CK elevation was significant in 25 % of subjects, peaking at the fourth day after exercise for 80 % of those. Such exercises should not be regarded as trivial and "easy" as they are advertised, since they can provoke important anaerobia and CK elevation. Many fragile patients or patients treated for cardiovascular disease could benefit from WBV but it is important to recognise these potential effects, especially in those treated with statins, known to cause a myopathy and CK elevation. Before considering a side effect of an important therapeutic agent, doctors should be aware of the possible interaction with not-so-harmless exercising machines.

The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography/mass spectrometry and chemometric tools

Cannabis cultivation in order to produce drugs is forbidden in Switzerland. Thus, law enforcement authorities regularly ask forensic laboratories to determinate cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. As required by the EU official analysis protocol the THC rate of cannabis is measured from the flowers at maturity. When laboratories are confronted to seedlings, they have to lead the plant to maturity, meaning a time consuming and costly procedure. This study investigated the discrimination of fibre type from drug type Cannabis seedlings by analysing the compounds found in their leaves and using chemometrics tools. 11 legal varieties allowed by the Swiss Federal Office for Agriculture and 13 illegal ones were greenhouse grown and analysed using a gas chromatograph interfaced with a mass spectrometer. Compounds that show high discrimination capabilities in the seedlings have been identified and a support vector machines (SVMs) analysis was used to classify the cannabis samples. The overall set of samples shows a classification rate above 99% with false positive rates less than 2%. This model allows then discrimination between fibre and drug type Cannabis at an early stage of growth. Therefore it is not necessary to wait plants' maturity to quantify their amount of THC in order to determine their chemotype. This procedure could be used for the control of legal (fibre type) and illegal (drug type) Cannabis production.

Monitoring of posture allocations and activities by a shoe-based wearable sensor.

Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) without significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.

Beliefs about and attitudes toward gambling in French-speaking Switzerland.

The present study aims to analyze attitudes and beliefs of the French-speaking general Swiss population (n = 2500; female n = 1280; mean age = 43 years) as regards gambling, which are to date almost exclusively studied in the North American and Australian contexts. Beliefs related to gambling include the perception of the effectiveness of preventive measures toward gambling, the comparative risk assessment of different addictive behaviors, the perceived risks of different types of gambling and attitudes are related to the gambler's personality. The general population perceived gambling rather negatively and was conscious of the potential risks of gambling; indeed, 59.0% of the sample identified gambling as an addictive practice. Slot machines were estimated to bear the highest risk. Compared with women and older people, men and young people indicated more positive beliefs about gambling; they perceived gambling as less addictive, supported structural preventive measures less often, and perceived gambling as a less serious problem for society. Gamblers were more likely to put their practices into perspective, perceiving gambling more positively than non-gamblers. General population surveys on such beliefs can deliver insights into preventive actions that should be targeted to young men who showed more favorable views of gambling, which have been shown to be associated with increased risk for problematic gambling.

Sähkökoneen staattorikäämityksen kehittäminen

Tämä insinöörityö tehtiin ABB:n Pitäjänmäen konetehtaalle Tahtikoneet-tulosyksikölle. Työssä tutkittiin mahdollisuuksia valmistaa murtovakovyyhtejä kestomagneettituuligeneraattoreihin, joissa vakoluku on alle yhden. Työ tehtiin tutustumalla aluksi Pitäjänmäen konetehtaan käytössä oleviin vyyhden valmistus- ja käämintämenetelmiin. Lisäksi tutkittiin erilaisia mahdollisia murtovakovyyhden valmistusmenetelmiä, joista lupaavimpia myös kokeiltiin. Saatujen kokemusten pohjalta valittiin vyyhden valmistusmenetelmä, jonka mukaan valmistettiin koe-erä. Koe-erälle suoritettiin mittauksia, joilla varmistettiin niiden sähköinen kestävyys Työn tuloksena valitulla valmistusmenetelmällä valmistettiin vyyhdet prototyyppi tuuligeneraattorin.

On prediction of individual sequences

Sequential randomized prediction of an arbitrary binary sequence isinvestigated. No assumption is made on the mechanism of generating the bit sequence. The goal of the predictor is to minimize its relative loss, i.e., to make (almost) as few mistakes as the best ``expert'' in a fixed, possibly infinite, set of experts. We point out a surprising connection between this prediction problem and empirical process theory. First, in the special case of static (memoryless) experts, we completely characterize the minimax relative loss in terms of the maximum of an associated Rademacher process. Then we show general upper and lower bounds on the minimaxrelative loss in terms of the geometry of the class of experts. As main examples, we determine the exact order of magnitude of the minimax relative loss for the class of autoregressive linear predictors and for the class of Markov experts.

Solving two production scheduling problems with sequence-dependent set-up times

In today’s competitive markets, the importance of goodscheduling strategies in manufacturing companies lead to theneed of developing efficient methods to solve complexscheduling problems.In this paper, we studied two production scheduling problemswith sequence-dependent setups times. The setup times areone of the most common complications in scheduling problems,and are usually associated with cleaning operations andchanging tools and shapes in machines.The first problem considered is a single-machine schedulingwith release dates, sequence-dependent setup times anddelivery times. The performance measure is the maximumlateness.The second problem is a job-shop scheduling problem withsequence-dependent setup times where the objective is tominimize the makespan.We present several priority dispatching rules for bothproblems, followed by a study of their performance. Finally,conclusions and directions of future research are presented.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Performance Report for Fiscal Year, 2008

I am pleased to present the performance report for the Iowa Department for the Blind for fiscal year 2008. This report is provided in compliance with sections 8E.210 and 216B.7 of the Code of Iowa. It contains valuable information about results achieved because of the services that we and our partners provided to blind and visually impaired Iowans during the past fiscal year in the areas of Vocational Rehabilitation, Independent Living, Library Services, and Resource Management. We determine our competitive success in a number of ways. We look at the federal standards and indicators to learn our ranking in relation to the performance of other public rehabilitation agencies. We compare our library's production and circulation figures with those from previous years to determine trends. We set our own standards for success by looking at such factors as the number of successful case closures, average hourly wage at case closure, skills training provided, and compliance with regulations. Results show that the Department is working positively toward achieving its strategic goals of increasing the independence and productivity of blind Iowans and improving access to information for blind Iowans. Major accomplishments of the year included:  The selection of our Library as one of eight libraries to receive the new digital talking book machines and books in digital media from the National Library Service for the Blind and Physically Handicapped. Priority for distribution of the machines is given to Library patrons who are veterans.  The Department, the Iowa Braille School, and the Department of Education have been promoting the new expanded core curriculum as part of their continued efforts to improve the coordination and delivery of services to blind and visually impaired students in Iowa.  The Department's five-year grant funded Pathfinders mentoring program ended this year. A total of 49 blind youths aged 16-26 were paired with successful blind adult mentors. Assessments of the program clearly showed that participation in the program had a measurable positive effect on the youth involved.  Finally, earnings ratios and the percentage of employment for vocational rehabilitation clients of the Department are among the best in the nation, as measured by the U.S. Rehabilitation Services Administration's standards and indicators for the year ended September 30, 2007. Overall, we met or exceeded 13 of 18 targets included in this report. A discussion of the Department's services, customers, and organizational structure, and budget appears in the "Department Overview" that follows. Information pertaining to performance results appears in the final section of this document. The success of the Department's programs is evident in the success achieved by blind Iowans. It is reflected in the many blind persons who can be seen traveling about independently, going to their jobs and to the community and family activities in which they participate. Sincerely, Karen A. Keninger, Director Iowa Department for the Blind

Aplicació de predicció del risc d'una cartera

L'objectiu principal del projecte és la creació d'una aplicació per a telèfons intel·ligents que intenti predir la volatilitat no atribuïble al mercat per tal de permetre a l'usuari crear portfolios òptims utilitzant tècniques d'intel·ligència artificial com són les Support Vector Machines (SVM). Una vegada s'hagi predit aquesta volatilitat es crearà un portfolio òptim amb el pes adequat de cada un dels valors, per tal d'obtenir una inversió amb el mínim risc possible.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).