Finitely coordinated models for low-temperature phases of amorphous systems

We introduce models of heterogeneous systems with finite connectivity defined on random graphs to capture finite-coordination effects on the low-temperature behaviour of finite-dimensional systems. Our models use a description in terms of small deviations of particle coordinates from a set of reference positions, particularly appropriate for the description of low-temperature phenomena. A Born-von Karman-type expansion with random coefficients is used to model effects of frozen heterogeneities. The key quantity appearing in the theoretical description is a full distribution of effective single-site potentials which needs to be determined self-consistently. If microscopic interactions are harmonic, the effective single-site potentials turn out to be harmonic as well, and the distribution of these single-site potentials is equivalent to a distribution of localization lengths used earlier in the description of chemical gels. For structural glasses characterized by frustration and anharmonicities in the microscopic interactions, the distribution of single-site potentials involves anharmonicities of all orders, and both single-well and double-well potentials are observed, the latter with a broad spectrum of barrier heights. The appearance of glassy phases at low temperatures is marked by the appearance of asymmetries in the distribution of single-site potentials, as previously observed for fully connected systems. Double-well potentials with a broad spectrum of barrier heights and asymmetries would give rise to the well-known universal glassy low-temperature anomalies when quantum effects are taken into account. © 2007 IOP Publishing Ltd.

An examination of the appropriateness of the contemporary strategic management models for the Greek small manufacturing enterprises

The starting point of the project was the observation that strategic management is absent in small businesses. The first objective of the project was to examine the reasons causing this situation in Greece, the second one, to examine the appropriateness of the contemporary models of strategic planning for the Greek S.M.E.s, and the third to examine the appropriateness of the alternative approaches to strategic management for the Greek S.M.E.s. The term appropriateness includes (a) the ability of managers to use the models and (b) the ability of the models to assist the managers. The results of the research indicate that none of the two above conditions exists, hence, it is suggested that the contemporary models of strategic management are inappropriate for the Greek S.M.E.s. Many previous research projects on the topic suggest that since the strategic decision making process in S.M.E.s is informal, the whole process is absent or ineffective. Current trends in S.M.E.s' strategic management do not consider the informality of the strategic decision making process as a kind of managerial illness, but as a managerial characteristic. The use of sophisticated data collection and analytical methods does not indicate successful strategic decisions, but it indicates the method large firms use to manage their strategy. According to the literature review, the S.M.E.s' managers avoid the use of the contemporary models of strategic management, because they do not have the knowledge, the resources or the time. Another thesis, expressed by some firms' specialists, suggests that small firms are different from large ones, hence their practice of strategic management should not follow the large firm's prototypes.

Effects of some 5-hydroxytryptamine and related ligands in anxiety models

Drugs acting at 5-HT receptors were evaluated on three animal models of anxiety. On the elevated X-maze test the majority of 5-HT1 agonists were found to be anxiogenic. However, ipsapirone was anxiolytic and buspirone and gepirone were inactive. The 5-HT2 agonist DOI and the 5-HT2 antagonist ritanserin were anxiolytic while ICI 169,369, a 5-HT2 antagonist was inactive. All 5-HT3 antagonists tested were inactive in this test, while the indirect serotomimetics zimeldine and fenfluramine were anxiogenic. Neither beta-adrenoceptor agonists nor antagonists had reproducible effects on anxiety in this model. Combined beta-1/beta-2 adrenoceptor antagonists reversed the anxiogenic effects of 8-OH-DPAT while selective beta-1 or beta-2 antagonists did not. On the social interaction model the 5-HT1 agonists 8-OH-DPAT, RU 24969 and 5-MeODMT were anxiogenic and ipsapirone was anxiolytic. The 5-HT2 agonist DOI and the beta-adrenoceptor- and 5-HT- antagonist pindolol were anxiolytic, while the 5-HT2 and 5-HT3 antagonists were inactive. In the marble burying test, the 5-HT upake inhibitors zimeldine, fluvoxamine, indalpine and citalopram, the 5-HT1B/5-HT1C agonists mCPP and TFMPP and the 5-HT2/5-HT1C agonist DOI reduced marble burying without affecting locomotor activity. 5-HT1A agonists and the 5-HT2 and 5-HT3 antagonists were without effect. Lesions of the dorsal raphe nucleus reversed the anxiogenic effects of 8-OH-DPAT in the X-maze model. The implication of these results for the understanding of the pharmacology of 5-HT in anxiety is discussed.

On the modulation of the effects of some 5 - HT - related agents in axiety models

The results of an investigation into how stressors interact with the action of serotonergic agents in animal models of anxiety are presented. Water deprivation and restraint both increased plasma corticosterone concentrations and elevated 5-HT turnover. In the elevated X-maze, water deprivation had a duration-dependent "anxiolytic" effect. The effect of restraint was dependent on the duration of restraint and was to inhibit maze exploration. Water-deprivation did not influence the action of diazepam or any 5-HT1A ligand in the X-maze. Restraint switched the "anxiogenic" effect of 8-0H-DPAT to either "anxiolytic" or inactive, depending on the time after the restraint when testing was performed. The Vogel conflict test detected an "anxiolytic" "anxiolytic"V"anxiolytic""anxiolytic" effect of buspirone which was additive with "anxiolytic" effects of pindolol and propranolol. Diazepam and fluoxetine were also active, but 8-0H-DPAT, ipsapirone, gepirone and yohimbine were inactive. In the elevated X-maze, "anxiogenic" responses to picrotoxin, flumazenil, RU 24969, CGS 12066B, fluoxetine and 8-0H-DPAT were detected. Other 5-HT1A ligands were inactive. Diazepam and corticosterone had "anxiolytic" effects. Increasing light intensity did not change behaviour on the elevated X-maze, but was able to reverse the effect of 8- OH-DPAT to an "anxiolytic" action. This effect was attributed to a presynaptic mechanism, because it was abolished by pCPA. The occurence of different behaviours in different reglons of the maze was shown to be susceptible to modulation by "anxiolytic" and "anxiogenic" drugs. These results are discussed in the context of there being at least two separate 5-HT mechanisms which are involved in the control of anxiety.

Comparative Analysis of Viscoelastic Models Involving Fractional Derivatives of Different Orders

Mathematics Subject Classification: 74D05, 26A33

A Public-Private Partnership strukturális modelljeinek összevetése = Comparison of Public-Private Partnership models

A tanulmány a PPP különböző strukturális modelljeinek csoportosítását mutatja be, az egyes típusok rövid rendszerező áttekintésével. A tipológiák vizsgálata hasznos ahhoz, hogy a PPP projektek struktúrájának kialakításakor a különböző lehetőségeket mérlegelni tudjuk. Többféle megközelítésben lehet a modelleket tipizálni. Az együttműködés célja alapján a hatékonyság-, a minőség- és a finanszírozás-orientált modellek a legelterjedtebbek, a kockázatmegosztás módja alapján BOT, DBFO és koncessziós változatok, a haszonmegosztás szabályozása alapján árplafon-szabályozású, közvetlen haszonszabályozású, fixdíjas és árnyékáras megoldások a leginkább bevettek. A tanulmány ezek elemző bemutatása alapján arra a következtetésre jut, hogy a gyakorlati megoldások a legtöbb esetben az elméleti típusok valamilyen kombinációját tartalmazzák, a konkrét eset feltételeinek megfelelően. Így a gyakorlatban a fix tipológiák helyett alkalmasabb úgy megközelítenünk a PPP-t, mint egy folyamatosan változó, a helyi igényekhez idomuló jelenséget. A haszonszabályozó tipológia kapcsán a tanulmány melléklete rövid áttekintést nyújt a PPP esetében kritikus méltányos haszon becslésének lehetséges megoldásairól is. = This study shows a categorization of the different structural models of Public-Private Partnership (PPP) projects. The typologies are useful to assess the available options when decisions on PPP project structures are made. There are different categorizing aspects. Based ont he key purpose of the partnership there are efficiency, quality and financing focused models. From a risk sharing point of view, BOT, DBFO and concession models are most typical. Regarding the regulation of returns price-cap models, ’open book’ models, fixed price and shadow pricing models are most common. Based on the analytical assessment of these, they study concludes that actual projects are mostly a combination of theoretical types, as required by the given case. Therefore in practice, it is more appropriate to approach PPP projects as a constantly shaping concept, adjustable to particular conditions. Supporting the approaches to the regulation of returns, an appendix of the study summarizes the different methods to estimate fair returns, a critical issue in PPP projects.

High frequency electric machine models for the evaluation of operational impacts in integrated drives

A high frequency physical phase variable electric machine model was developed using FE analysis. The model was implemented in a machine drive environment with hardware-in-the-loop. The novelty of the proposed model is that it is derived based on the actual geometrical and other physical information of the motor, considering each individual turn in the winding. This is the first attempt to develop such a model to obtain high frequency machine parameters without resorting to expensive experimental procedures currently in use. The model was used in a dynamic simulation environment to predict inverter-motor interaction. This includes motor terminal overvoltage, current spikes, as well as switching effects. In addition, a complete drive model was developed for electromagnetic interference (EMI) analysis and evaluation. This consists of the lumped parameter models of different system components, such as cable, inverter, and motor. The lumped parameter models enable faster simulations. The results obtained were verified by experimental measurements and excellent agreements were obtained. A change in the winding arrangement and its influence on the motor high frequency behavior has also been investigated. This was shown to have a little effect on the parameter values and in the motor high frequency behavior for equal number of turns. An accurate prediction of overvoltage and EMI in the design stages of the drive system would reduce the time required for the design modifications as well as for the evaluation of EMC compliance issues. The model can be utilized in the design optimization and insulation selection for motors. Use of this procedure could prove economical, as it would help designers develop and test new motor designs for the evaluation of operational impacts in various motor drive applications.

Development of physics-based models and design optimization of power electronic conversion systems

The main objective for physics based modeling of the power converter components is to design the whole converter with respect to physical and operational constraints. Therefore, all the elements and components of the energy conversion system are modeled numerically and combined together to achieve the whole system behavioral model. Previously proposed high frequency (HF) models of power converters are based on circuit models that are only related to the parasitic inner parameters of the power devices and the connections between the components. This dissertation aims to obtain appropriate physics-based models for power conversion systems, which not only can represent the steady state behavior of the components, but also can predict their high frequency characteristics. The developed physics-based model would represent the physical device with a high level of accuracy in predicting its operating condition. The proposed physics-based model enables us to accurately develop components such as; effective EMI filters, switching algorithms and circuit topologies [7]. One of the applications of the developed modeling technique is design of new sets of topologies for high-frequency, high efficiency converters for variable speed drives. The main advantage of the modeling method, presented in this dissertation, is the practical design of an inverter for high power applications with the ability to overcome the blocking voltage limitations of available power semiconductor devices. Another advantage is selection of the best matching topology with inherent reduction of switching losses which can be utilized to improve the overall efficiency. The physics-based modeling approach, in this dissertation, makes it possible to design any power electronic conversion system to meet electromagnetic standards and design constraints. This includes physical characteristics such as; decreasing the size and weight of the package, optimized interactions with the neighboring components and higher power density. In addition, the electromagnetic behaviors and signatures can be evaluated including the study of conducted and radiated EMI interactions in addition to the design of attenuation measures and enclosures.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Ocean acidification and the loss of phenolic substances in marine plants

Rising atmospheric CO2 often triggers the production of plant phenolics, including many that serve as herbivore deterrents, digestion reducers, antimicrobials, or ultraviolet sunscreens. Such responses are predicted by popular models of plant defense, especially resource availability models which link carbon availability to phenolic biosynthesis. CO2 availability is also increasing in the oceans, where anthropogenic emissions cause ocean acidification, decreasing seawater pH and shifting the carbonate system towards further CO2 enrichment. Such conditions tend to increase seagrass productivity but may also increase rates of grazing on these marine plants. Here we show that high CO2 / low pH conditions of OA decrease, rather than increase, concentrations of phenolic protective substances in seagrasses and eurysaline marine plants. We observed a loss of simple and polymeric phenolics in the seagrass Cymodocea nodosa near a volcanic CO2 vent on the Island of Vulcano, Italy, where pH values decreased from 8.1 to 7.3 and pCO2 concentrations increased ten-fold. We observed similar responses in two estuarine species, Ruppia maritima and Potamogeton perfoliatus, in in situ Free-Ocean-Carbon-Enrichment experiments conducted in tributaries of the Chesapeake Bay, USA. These responses are strikingly different than those exhibited by terrestrial plants. The loss of phenolic substances may explain the higher-than-usual rates of grazing observed near undersea CO2 vents and suggests that ocean acidification may alter coastal carbon fluxes by affecting rates of decomposition, grazing, and disease. Our observations temper recent predictions that seagrasses would necessarily be "winners" in a high CO2 world.

Results of wave modelling for Moreton Bay, Southeast Queensland, and a reef lagoon off Lizard Island, Great Barrier Reef, Australia

The distribution, abundance, behaviour, and morphology of marine species is affected by spatial variability in the wave environment. Maps of wave metrics (e.g. significant wave height Hs, peak energy wave period Tp, and benthic wave orbital velocity URMS) are therefore useful for predictive ecological models of marine species and ecosystems. A number of techniques are available to generate maps of wave metrics, with varying levels of complexity in terms of input data requirements, operator knowledge, and computation time. Relatively simple "fetch-based" models are generated using geographic information system (GIS) layers of bathymetry and dominant wind speed and direction. More complex, but computationally expensive, "process-based" models are generated using numerical models such as the Simulating Waves Nearshore (SWAN) model. We generated maps of wave metrics based on both fetch-based and process-based models and asked whether predictive performance in models of benthic marine habitats differed. Predictive models of seagrass distribution for Moreton Bay, Southeast Queensland, and Lizard Island, Great Barrier Reef, Australia, were generated using maps based on each type of wave model. For Lizard Island, performance of the process-based wave maps was significantly better for describing the presence of seagrass, based on Hs, Tp, and URMS. Conversely, for the predictive model of seagrass in Moreton Bay, based on benthic light availability and Hs, there was no difference in performance using the maps of the different wave metrics. For predictive models where wave metrics are the dominant factor determining ecological processes it is recommended that process-based models be used. Our results suggest that for models where wave metrics provide secondarily useful information, either fetch- or process-based models may be equally useful.

Seawater carbonate chemistry and structural integrity of the coralline algae Lithothamnion glaciale in a laboratory experiment

The uptake of anthropogenic emission of carbon dioxide is resulting in a lowering of the carbonate saturation state and a drop in ocean pH. Understanding how marine calcifying organisms such as coralline algae may acclimatize to ocean acidification is important to understand their survival over the coming century. We present the first long-term perturbation experiment on the cold-water coralline algae, which are important marine calcifiers in the benthic ecosystems particularly at the higher latitudes. Lithothamnion glaciale, after three months incubation, continued to calcify even in undersaturated conditions with a significant trend towards lower growth rates with increasing pCO2. However, the major changes in the ultra-structure occur by 589 µatm (i.e. in saturated waters). Finite element models of the algae grown at these heightened levels show an increase in the total strain energy of nearly an order of magnitude and an uneven distribution of the stress inside the skeleton when subjected to similar loads as algae grown at ambient levels. This weakening of the structure is likely to reduce the ability of the alga to resist boring by predators and wave energy with severe consequences to the benthic community structure in the immediate future (50 years).

Evaluating demographic models for goat domestication using mtDNA sequences

Routes of migration and exchange are important factors in the debate about how the Neolithic transition spread into Europe. Studying the genetic diversity of livestock can help in tracing back some of these past events. Notably, domestic goat (Capra hircus) did not have any wild progenitors (Capra aegagrus) in Europe before their arrival from the Near East. Studies of mitochondrial DNA have shown that the diversity in European domesticated goats is a subset of that in the wild, underlining the ancestral relationship between both populations. Additionally, an ancient DNA study on Neolithic goat remains has indicated that a high level of genetic diversity was already present early in the Neolithic in northwestern Mediterranean sites. We used coalescent simulations and approximate Bayesian computation, conditioned on patterns of modern and ancient mitochondrial DNA diversity in domesticated and wild goats, to test a series of simplified models of the goat domestication process. Specifically, we ask if domestic goats descend from populations that were distinct prior to domestication. Although the models we present require further analyses, preliminary results indicate that wild and domestic goats are more likely to descend from a single ancestral wild population that was managed 11,500 years before present, and that serial founding events characterise the spread of Capra hircus into Europe.

The Status of Multi-Dimensional Core-Collapse Supernova Models

Models of neutrino-driven core-collapse supernova explosions have matured considerably in recent years. Explosions of low-mass progenitors can routinely be simulated in 1D, 2D, and 3D. Nucleosynthesis calculations indicate that these supernovae could be contributors of some lighter neutron-rich elements beyond iron. The explosion mechanism of more massive stars remains under investigation, although first 3D models of neutrino-driven explosions employing multi-group neutrino transport have become available. Together with earlier 2D models and more simplified 3D simulations, these have elucidated the interplay between neutrino heating and hydrodynamic instabilities in the post-shock region that is essential for shock revival. However, some physical ingredients may still need to be added/improved before simulations can robustly explain supernova explosions over a wide range of progenitors. Solutions recently suggested in the literature include uncertainties in the neutrino rates, rotation, and seed perturbations from convective shell burning. We review the implications of 3D simulations of shell burning in supernova progenitors for the ‘perturbations-aided neutrino-driven mechanism,’ whose efficacy is illustrated by the first successful multi-group neutrino hydrodynamics simulation of an 18 solar mass progenitor with 3D initial conditions. We conclude with speculations about the impact of 3D effects on the structure of massive stars through convective boundary mixing.