Attractive silences in cricket courtship song : mate choice using a short-range signal /

Matings systems using signals for sexual communication have been studied extensively and results commonly suggest that females use these signals for locating males, species-identification, and mate choice. Although numerous mating systems employ multiple signals, research has generally focused on long-range signals perhaps due to their prominence and ease of study. This study focused on the short-range acoustic courtship song of crickets. The results presented here suggest this signal is under selection by female choice. Females mated preferentially with males having shorter silences between the two types of ticks within the song. The length of these silences (Gap 1) was correlated with male condition such that males having long silences were significantly lower in mass with respect to body size when compared to males having short silences. Both Gap 1 length and male condition were significantly repeatable within males over time suggesting the possibility these traits have a genetic basis. This study is the first empirical study to test female preferences within the natural variation of the courtship song. It now appears, at least in crickets, that both the longand short-range signals of a multi-signal mating system may contribute to male mating success.

La construcción de sentido del inicio de un cambio estratégico en las organizaciones: el caso de la Universidad del Rosario

A través de un caso de estudio se explora cómo la construcción de sentido de un grupo de directivos, bajo una misma inspiración, generó el inicio de un cambio estratégico en una prestigiosa y reconocida universidad colombiana, la Universidad del Rosario. Una institución que en un momento determinado notó que estaba siendo percibida dentro del sector de la educación superior como pequeña, estática en el avance de algunas disciplinas del conocimiento y conservadora; en otras palabras, que estaba perdiendo el reconocimiento que usualmente la había acompañado. A través del estudio de este caso se utilizó la técnica de análisis de discurso para comprender la construcción de sentido del inicio de un cambio estratégico en las organizaciones. Esta técnica permitió analizar la información cualitativa derivada de las entrevistas que se realizaron en profundidad a la cúpula de directivos de la institución y a algunos destacados representantes del sector de la Educación Superior en Colombia. Los resultados sugieren que se hicieron presentes, efectivamente, algunas condiciones específicas que marcaron el inicio de un cambio estratégico en la institución y un viraje en su identidad e imagen. Hechos que se sustentaron en los miembros de un equipo que procuró interpretar y comprender los cambios existentes en el entorno global y local, y asimilar, igualmente, algunos destacados retos que se planteaban por aquella época, al interior de la propia Universidad

The sample autocorrelation function and the detection of long-memory processes

The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to be identified using the sum of the sample autocorrelations, as usually defined. The reason for this is that the sample sum is a predetermined constant for any stationary time series; a result that is independent of the sample size. Diagnostic or estimation procedures, such as those in the frequency domain, that embed this sum are equally open to this criticism. We develop this result in the context of long memory, extending it to the implications for the spectral density function and the variance of partial sums of a stationary stochastic process. The results are further extended to higher order sample autocorrelations and the bispectral density. The corresponding result is that the sum of the third order sample (auto) bicorrelations at lags h,k≥1, is also a predetermined constant, different from that in the second order case, for any stationary time series of arbitrary length.

A real-time measurement system for long-life flood monitoring and warning applications

A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data in many sparse locations during flood events.

Approach to Equilibrium for a Class of Random Quantum Models of Infinite Range

We consider random generalizations of a quantum model of infinite range introduced by Emch and Radin. The generalizations allow a neat extension from the class l (1) of absolutely summable lattice potentials to the optimal class l (2) of square summable potentials first considered by Khanin and Sinai and generalised by van Enter and van Hemmen. The approach to equilibrium in the case of a Gaussian distribution is proved to be faster than for a Bernoulli distribution for both short-range and long-range lattice potentials. While exponential decay to equilibrium is excluded in the nonrandom l (1) case, it is proved to occur for both short and long range potentials for Gaussian distributions, and for potentials of class l (2) in the Bernoulli case. Open problems are discussed.

Multiple attractors, long chaotic transients, and failure in small-world networks of excitable neurons.

We study the dynamical states of a small-world network of recurrently coupled excitable neurons, through both numerical and analytical methods. The dynamics of this system depend mostly on both the number of long-range connections or ?shortcuts?, and the delay associated with neuronal interactions. We find that persistent activity emerges at low density of shortcuts, and that the system undergoes a transition to failure as their density reaches a critical value. The state of persistent activity below this transition consists of multiple stable periodic attractors, whose number increases at least as fast as the number of neurons in the network. At large shortcut density and for long enough delays the network dynamics exhibit exceedingly long chaotic transients, whose failure times follow a stretched exponential distribution. We show that this functional form arises for the ensemble-averaged activity if the failure time for each individual network realization is exponen- tially distributed

Long-term chemical characterization of aerosols at CVAO from 2007 to 2011

The first long-term aerosol sampling and chemical characterization results from measurements at the Cape Verde Atmospheric Observatory (CVAO) on the island of São Vicente are presented and are discussed with respect to air mass origin and seasonal trends. In total 671 samples were collected using a high-volume PM10 sampler on quartz fiber filters from January 2007 to December 2011. The samples were analyzed for their aerosol chemical composition, including their ionic and organic constituents. Back trajectory analyses showed that the aerosol at CVAO was strongly influenced by emissions from Europe and Africa, with the latter often responsible for high mineral dust loading. Sea salt and mineral dust dominated the aerosol mass and made up in total about 80% of the aerosol mass. The 5-year PM10 mean was 47.1 ± 55.5 µg/m**2, while the mineral dust and sea salt means were 27.9 ± 48.7 and 11.1 ± 5.5 µg/m**2, respectively. Non-sea-salt (nss) sulfate made up 62% of the total sulfate and originated from both long-range transport from Africa or Europe and marine sources. Strong seasonal variation was observed for the aerosol components. While nitrate showed no clear seasonal variation with an annual mean of 1.1 ± 0.6 µg/m**3, the aerosol mass, OC (organic carbon) and EC (elemental carbon), showed strong winter maxima due to strong influence of African air mass inflow. Additionally during summer, elevated concentrations of OM were observed originating from marine emissions. A summer maximum was observed for non-sea-salt sulfate and was connected to periods when air mass inflow was predominantly of marine origin, indicating that marine biogenic emissions were a significant source. Ammonium showed a distinct maximum in spring and coincided with ocean surface water chlorophyll a concentrations. Good correlations were also observed between nss-sulfate and oxalate during the summer and winter seasons, indicating a likely photochemical in-cloud processing of the marine and anthropogenic precursors of these species. High temporal variability was observed in both chloride and bromide depletion, differing significantly within the seasons, air mass history and Saharan dust concentration. Chloride (bromide) depletion varied from 8.8 ± 8.5% (62 ± 42%) in Saharan-dust-dominated air mass to 30 ± 12% (87 ± 11%) in polluted Europe air masses. During summer, bromide depletion often reached 100% in marine as well as in polluted continental samples. In addition to the influence of the aerosol acidic components, photochemistry was one of the main drivers of halogenide depletion during the summer; while during dust events, displacement reaction with nitric acid was found to be the dominant mechanism. Positive matrix factorization (PMF) analysis identified three major aerosol sources: sea salt, aged sea salt and long-range transport. The ionic budget was dominated by the first two of these factors, while the long-range transport factor could only account for about 14% of the total observed ionic mass.

How long is the memory of the US stock market?

The Efficient Market Hypothesis (EMH), one of the most important hypothesis in financial economics, argues that return rates have no memory (correlation) which implies that agents cannot make abnormal profits in financial markets, due to the possibility of arbitrage operations. With return rates for the US stock market, we corroborate the fact that with a linear approach, return rates do not show evidence of correlation. However, linear approaches might not be complete or global, since return rates could suffer from nonlinearities. Using detrended cross-correlation analysis and its correlation coefficient, a methodology which analyzes long-range behavior between series, we show that the long-range correlation of return rates only ends in the 149th lag, which corresponds to about seven months. Does this result undermine the EMH?

Do Global CO2 Emissions from Fossil-Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis

In this article we use an autoregressive fractionally integrated moving average approach to measure the degree of fractional integration of aggregate world CO2 emissions and its five components – coal, oil, gas, cement, and gas flaring. We find that all variables are stationary and mean reverting, but exhibit long-term memory. Our results suggest that both coal and oil combustion emissions have the weakest degree of long-range dependence, while emissions from gas and gas flaring have the strongest. With evidence of long memory, we conclude that transitory policy shocks are likely to have long-lasting effects, but not permanent effects. Accordingly, permanent effects on CO2 emissions require a more permanent policy stance. In this context, if one were to rely only on testing for stationarity and non-stationarity, one would likely conclude in favour of non-stationarity, and therefore that even transitory policy shocks

An investigation of nucleation events in a coastal urban environment in the southern hemisphere

The occurrence of and conditions favourable to nucleation were investigated at an industrial and commercial coastal location in Brisbane, Australia during five different campaigns covering a total period of 13 months. To identify potential nucleation events, the difference in number concentration in the size range 14-30 nm (N14-30) between consecutive observations was calculated using first-order differencing. The data showed that nucleation events were a rare occurrence, and that in the absence of nucleation the particle number was dominated by particles in the range 30-300 nm. In many instances, total particle concentration declined during nucleation. There was no clear pattern in change in NO and NO2 concentrations during the events. SO2 concentration, in the majority of cases, declined during nucleation but there were exceptions. Most events took place in summer, followed by winter and then spring, and no events were observed for the autumn campaigns. The events were associated with sea breeze and long-range transport. Roadside emissions, in contrast, did not contribute to nucleation, probably due to the predominance of particles in the range 50-100 nm associated with these emissions.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Why new business development projects fail : coping with the differences of technological versus market knowledge

Managing through projects has become important for generating new knowledge to cope with technological and market discontinuities. This paper examines how the fit between the creation of technological and market knowledge and important project management characteristics, i.e. project autonomy and completion criteria, influences the success of new business development (NBD) projects. In-depth longitudinal case research on NBD projects commercialised from 1993 to 2003 in the consumer electronics industry highlights that project management characteristics focusing only on the creation of technological knowledge contributed to the failure of those NBD projects that required new market knowledge as well. The findings indicate that senior management support and engaging in an alliance with partners possessing complementary market knowledge can offset this misalignment of the organisation of NBD projects.

Soft-biometrics : unconstrained authentication in a surveillance environment

Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

Influence of smectite suspension structure on sheet orientation in dry sediments : XRD and AFM applications

The structure-building phenomena within clay aggregates are governed by forces acting between clay particles. Measurements of such forces are important to understand in order to manipulate the aggregate structure for applications such as dewatering of mineral processing tailings. A parallel particle orientation is required when conducting XRD investigation on the oriented samples and conduct force measurements acting between basal planes of clay mineral platelets using at. force microscopy (AFM). To investigate how smectite clay platelets were oriented on silicon wafer substrate when dried from suspension range of methods like SEM, XRD and AFM were employed. From these investigations, we conclude that high clay concns. and larger particle diams. (up to 5 μm) in suspension result in random orientation of platelets in the substrate. The best possible laminar orientation in the clay dry film, represented in the XRD 0 0 1/0 2 0 intensity ratio of 47 was obtained by drying thin layers from 0.02 wt.% clay suspensions of the natural pH. Conducted AFM investigations show that smectite studied in water based electrolytes show very long-range repulsive forces lower in strength than electrostatic forces from double-layer repulsion. It was suggested that these forces may have structural nature. Smectite surface layers rehydrate in water environment forms surface gel with spongy and cellular texture which cushion approaching AFM probe. This structural effect can be measured in distances larger than 1000 nm from substrate surface and when probe penetrate this gel layer, structural linkages are forming between substrate and clay covered probe. These linkages prevent subsequently smooth detachments of AFM probe on way back when retrieval. This effect of tearing new formed structure apart involves larger adhesion-like forces measured in retrieval. It is also suggested that these effect may be enhanced by the nano-clay particles interaction.