A hybrid algorithm combining metaheuristic with Monte Carlo simulation for solving the Stochastic Flow Shop problem

In this paper, a hybrid simulation-based algorithm is proposed for the StochasticFlow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation to the latter. In order to achieve this goal, we rely on Monte Carlo Simulation and an adapted version of a deterministic heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.

A hybrid algorithm combining metaheuristic with Monte Carlo simulation for solving the Stochastic Flow Shop problem

In this paper, a hybrid simulation-based algorithm is proposed for the StochasticFlow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation to the latter. In order to achieve this goal, we rely on Monte Carlo Simulation and an adapted version of a deterministic heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.

Asymmetric stochastic switching driven by intrinsic molecular noise

Low-copy-number molecules are involved in many functions in cells. The intrinsic fluctuations of these numbers can enable stochastic switching between multiple steady states, inducing phenotypic variability. Herein we present a theoretical and computational study based on Master Equations and Fokker-Planck and Langevin descriptions of stochastic switching for a genetic circuit of autoactivation. We show that in this circuit the intrinsic fluctuations arising from low-copy numbers, which are inherently state-dependent, drive asymmetric switching. These theoretical results are consistent with experimental data that have been reported for the bistable system of the gallactose signaling network in yeast. Our study unravels that intrinsic fluctuations, while not required to describe bistability, are fundamental to understand stochastic switching and the dynamical relative stability of multiple states.

Adding tobacco to cannabis: its frequency and likely implications.

INTRODUCTION: Young cannabis users are at increased risk of later cigarette initiation and progression to nicotine addiction. The present study addresses the frequency at which mulling (adding tobacco to cannabis smoked as joints) is performed and in which way this practice varies according to cigarette smoking status. METHODS: Data were issued from the Swiss 2007 European School Survey Project on Alcohol and other Drugs (ESPAD). A total of 881 past month cannabis users (mean age 15 years, boys 60.1%) were inquired on mulling using an anonymous self-administered questionnaire. Participants were further grouped according to their cigarette smoking status (daily, occasional, former, and never-smokers). RESULTS: Four of every 5 cannabis users depicted mulling as frequently performed. The highest occurrence was found among daily cigarette smokers (DSC; 90.3%), while former cigarette smokers reported the lowest (58.9%). The multinomial logistic regression showed DSC more likely reporting mulling as frequent compared with never-smokers (risk ratio = 3.56 [95% CI 1.55-8.21]). Conclusions: Mulling appears to be a very common process among young cannabis users, especially among concomitant cigarette smokers. Nevertheless, the majority of cigarette abstainers also reported frequently adding tobacco to the cannabis they smoke. Because it may represent a significant exposition to nicotine, mulling should be taken into account when assessing substance use among adolescents and in supporting their quitting attempts.

New directions in Stochastic Multicriteria Acceptability Analysis

Decisions taken in modern organizations are often multi-dimensional, involving multiple decision makers and several criteria measured on different scales. Multiple Criteria Decision Making (MCDM) methods are designed to analyze and to give recommendations in this kind of situations. Among the numerous MCDM methods, two large families of methods are the multi-attribute utility theory based methods and the outranking methods. Traditionally both method families require exact values for technical parameters and criteria measurements, as well as for preferences expressed as weights. Often it is hard, if not impossible, to obtain exact values. Stochastic Multicriteria Acceptability Analysis (SMAA) is a family of methods designed to help in this type of situations where exact values are not available. Different variants of SMAA allow handling all types of MCDM problems. They support defining the model through uncertain, imprecise, or completely missing values. The methods are based on simulation that is applied to obtain descriptive indices characterizing the problem. In this thesis we present new advances in the SMAA methodology. We present and analyze algorithms for the SMAA-2 method and its extension to handle ordinal preferences. We then present an application of SMAA-2 to an area where MCDM models have not been applied before: planning elevator groups for high-rise buildings. Following this, we introduce two new methods to the family: SMAA-TRI that extends ELECTRE TRI for sorting problems with uncertain parameter values, and SMAA-III that extends ELECTRE III in a similar way. An efficient software implementing these two methods has been developed in conjunction with this work, and is briefly presented in this thesis. The thesis is closed with a comprehensive survey of SMAA methodology including a definition of a unified framework.

On the Condition Monitoring of Induction machines

The research of condition monitoring of electric motors has been wide for several decades. The research and development at universities and in industry has provided means for the predictive condition monitoring. Many different devices and systems are developed and are widely used in industry, transportation and in civil engineering. In addition, many methods are developed and reported in scientific arenas in order to improve existing methods for the automatic analysis of faults. The methods, however, are not widely used as a part of condition monitoring systems. The main reasons are, firstly, that many methods are presented in scientific papers but their performance in different conditions is not evaluated, secondly, the methods include parameters that are so case specific that the implementation of a systemusing such methods would be far from straightforward. In this thesis, some of these methods are evaluated theoretically and tested with simulations and with a drive in a laboratory. A new automatic analysis method for the bearing fault detection is introduced. In the first part of this work the generation of the bearing fault originating signal is explained and its influence into the stator current is concerned with qualitative and quantitative estimation. The verification of the feasibility of the stator current measurement as a bearing fault indicatoris experimentally tested with the running 15 kW induction motor. The second part of this work concentrates on the bearing fault analysis using the vibration measurement signal. The performance of the micromachined silicon accelerometer chip in conjunction with the envelope spectrum analysis of the cyclic bearing faultis experimentally tested. Furthermore, different methods for the creation of feature extractors for the bearing fault classification are researched and an automatic fault classifier using multivariate statistical discrimination and fuzzy logic is introduced. It is often important that the on-line condition monitoring system is integrated with the industrial communications infrastructure. Two types of a sensor solutions are tested in the thesis: the first one is a sensor withcalculation capacity for example for the production of the envelope spectra; the other one can collect the measurement data in memory and another device can read the data via field bus. The data communications requirements highly depend onthe type of the sensor solution selected. If the data is already analysed in the sensor the data communications are needed only for the results but in the other case, all measurement data need to be transferred. The complexity of the classification method can be great if the data is analysed at the management level computer, but if the analysis is made in sensor itself, the analyses must be simple due to the restricted calculation and memory capacity.

Electrical Machines' Market in Russia

Venäjän valtion osuus maailmantaloudesta on pieni verrattuna sen maantieteelliseen kokoon, väkilukuun ja luonnonvaroihin. Sitä pidetään kuitenkin yhtenä tulevaisuuden merkittävistä kasvumarkkinoista. Venäjällä on tyypillisesti teollisuutta, joka hyödyntää luonnonvaroja ja tuottaa raaka-aineita sekä kotimaan että ulkomaiden markkinoille. Tällaisia tyypillisiä teollisuudenaloja Venäjällä ovat kaivos- ja metsäteollisuus sekä kemikaalien- kaasun- ja öljyntuotanto. Myös näiden teollisuusalojen tarvitsemien tuotantolaitteiden ja koneiden valmistusta on Venäjällä. Näitä koneita viedään Venäjältä entisiin neuvostovaltioihin ja päinvastoin. Tässä diplomityössä tutkitaan sähkömoottorien markkinapotentiaalia ja kilpailutilannetta Venäjällä. Venäjän osalta perehdytään sen kansantalouden tilaan ja tutkitaan sähkökonemarkkinoiden kokoa segmenteittäin monien erilähteiden avulla. Venäjän arvioidaan olevan erittäin potentiaalinen ja kasvava markkina-alue. Diplomityössä selvitetään ostoprosessia Venäjällä ja sähkökonemarkkinoiden ominaisuuksia kyseisellä alueella.

The Effect of Adding CO2 to Hypoxic Inspired Gas on Cerebral Blood Flow Velocity and Breathing during Incremental Exercise

Hypoxia increases the ventilatory response to exercise, which leads to hyperventilation-induced hypocapnia and subsequent reduction in cerebral blood flow (CBF). We studied the effects of adding CO2 to a hypoxic inspired gas on CBF during heavy exercise in an altitude naïve population. We hypothesized that augmented inspired CO2 and hypoxia would exert synergistic effects on increasing CBF during exercise, which would improve exercise capacity compared to hypocapnic hypoxia. We also examined the responsiveness of CO2 and O2 chemoreception on the regulation ventilation (E) during incremental exercise. We measured middle cerebral artery velocity (MCAv; index of CBF), E, end-tidal PCO2, respiratory compensation threshold (RC) and ventilatory response to exercise (E slope) in ten healthy men during incremental cycling to exhaustion in normoxia and hypoxia (FIO2 = 0.10) with and without augmenting the fraction of inspired CO2 (FICO2). During exercise in normoxia, augmenting FICO2 elevated MCAv throughout exercise and lowered both RC onset andE slope below RC (P<0.05). In hypoxia, MCAv and E slope below RC during exercise were elevated, while the onset of RC occurred at lower exercise intensity (P<0.05). Augmenting FICO2 in hypoxia increased E at RC (P<0.05) but no difference was observed in RC onset, MCAv, or E slope below RC (P>0.05). The E slope above RC was unchanged with either hypoxia or augmented FICO2 (P>0.05). We found augmenting FICO2 increased CBF during sub-maximal exercise in normoxia, but not in hypoxia, indicating that the 'normal' cerebrovascular response to hypercapnia is blunted during exercise in hypoxia, possibly due to an exhaustion of cerebral vasodilatory reserve. This finding may explain the lack of improvement of exercise capacity in hypoxia with augmented CO2. Our data further indicate that, during exercise below RC, chemoreception is responsive, while above RC the ventilatory response to CO2 is blunted.