Factors affecting the design and construction of Lamella suspen-dome systems

The suspen-dome system is a new structural form that has become popular in the construction of long-span roof structures. These domes are very slender and lightweight, their configuration is complicated, and hence sequential consideration in the structural design is needed. This paper focuses on these considerations, which include the method for designing cable prestress force, a simplified analysis method, and the estimation of buckling capacity. Buckling is one of the most important problems for dome structures. This paper presents the findings of an intensive buckling study of the Lamella suspen-dome system that takes geometric imperfection, asymmetric loading, rise-to-span ratio, and connection rigidity into consideration. Finally, suggested design and construction guidelines are given in the conclusion of this paper. (c) 2005 Elsevier Ltd. All rights reserved.

Power flow based monetary flow method for electricity transmission and wheeling pricing

This paper proposes a transmission and wheeling pricing method based on the monetary flow tracing along power flow paths: the monetary flow-monetary path method. Active and reactive power flows are converted into monetary flows by using nodal prices. The method introduces a uniform measurement for transmission service usages by active and reactive powers. Because monetary flows are related to the nodal prices, the impacts of generators and loads on operation constraints and the interactive impacts between active and reactive powers can be considered. Total transmission service cost is separated into more practical line-related costs and system-wide cost, and can be flexibly distributed between generators and loads. The method is able to reconcile transmission service cost fairly and to optimize transmission system operation and development. The case study on the IEEE 30 bus test system shows that the proposed pricing method is effective in creating economic signals towards the efficient use and operation of the transmission system. (c) 2005 Elsevier B.V. All rights reserved.

Conditional release: A less restrictive alternative to hospitalization?

Objective: This study examined conditional release - that is, involuntary outpatient commitment orders upon release from hospitalization - as a least restrictive alternative to psychiatric hospitalization in Victoria, Australia. Methods: Records were obtained from the Victorian Psychiatric Case Register for patients who experienced psychiatric hospitalization: between 1990 and 2000 a total of 8,879 patients were given conditional release and 16,094 were not. Results: Compared with the group that was hospitalized but did not receive a conditional release, the group that received a conditional release was more likely to have more prior hospitalizations of greater than average duration. Patients with schizophrenia were more likely to be given conditional release. Patients given conditional release experienced a care pattern involving briefer inpatient episodes (8.3 fewer days per episode), more inpatient days, and longer duration of restrictive care - that is, combined inpatient and conditional release periods (5.1 more days per month in care). Conclusions: For patients at risk of long-term hospitalization, conditional release may help to shorten inpatient episodes by providing a least restrictive alternative to continued hospitalization. However, patients who were given conditional release doubled the amount of days they spent under restrictive care, compared with the amount of time they previously spent in the hospital before entering a period of combined inpatient and conditional release commitment. Additional oversight may have led to more frequent hospitalization. This consequence raises new questions regarding the possible benefits of such extended oversight and new challenges for release planning using conditional release as a least restrictive method of care.

Estimation and mapping of foliar chemical levels in a coastal eucalypt forest using CASI data

Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

Good-turing estimation for the frequentist n-tuple classifier

We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

A hybrid genetic algorithm for the multi-depot vehicle routing problem

The distribution of finished products from depots to customers is a practical and challenging problem in logistics management. Better routing and scheduling decisions can result in higher level of customer satisfaction because more customers can be served in a shorter time. The distribution problem is generally formulated as the vehicle routing problem (VRP). Nevertheless, there is a rigid assumption that there is only one depot. In cases, for instance, where a logistics company has more than one depot, the VRP is not suitable. To resolve this limitation, this paper focuses on the VRP with multiple depots, or multi-depot VRP (MDVRP). The MDVRP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To deal with the problem efficiently, two hybrid genetic algorithms (HGAs) are developed in this paper. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method and the nearest neighbor heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different problem sizes. It is proved that the performance of HGA2 is superior to that of HGA1 in terms of the total delivery time.

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak.

Fiber Bragg grating structures inscribed using 800nm femtosecond laser in single- and multi-core mid-IR glass fibers with their spectral, thermal and strain properties

Single- and multi-core passive and active germanate and tellurite glass fibers represent a new class of fiber host for in-fiber photonics devices and applications in mid-IR wavelength range, which are in increasing demand. Fiber Bragg grating (FBG) structures have been proven as one of the most functional in-fiber devices and have been mass-produced in silicate fibers by UV-inscription for almost countless laser and sensor applications. However, because of the strong UV absorption in germanate and tellurite fibers, FBG structures cannot be produced by UVinscription. In recent years femtosecond (fs) lasers have been developed for laser machining and microstructuring in a variety of glass fibers and planar substrates. A number of papers have been reported on fabrication of FBGs and long-period gratings in optical fibers and also on the photosensitivity mechanism using 800nm fs lasers. In this paper, we demonstrate for the first time the fabrication of FBG structures created in passive and active single- and three-core germanate and tellurite glass fibers by using 800nm fs-inscription and phase mask technique. With a fs peak power intensity in the order of 1011W/cm2, the FBG spectra with 2nd and 3rd order resonances at 1540nm and 1033nm in a single-core germanate glass fiber and 2nd order resonances between ~1694nm and ~1677nm with strengths up to 14dB in all three cores of three-core passive and active tellurite fibers were observed. Thermal and strain properties of the FBGs made in these mid-IR glass fibers were characterized, showing an average temperature responsivity of ~20pm/°C and a strain sensitivity of 1.219±0.003pm/µe.

Corporate restructuring: empirical evidence from the UK

Corporate restructuring is perceived as a challenge to research. Prior studies do not provide conclusive evidence regarding the effects of restructuring. Since there are discernible findings, this research attempts to examine the effects of restructuring events amongst the UK listed firms. The sample firms are listed in the LSE and London AIM stock exchange. Only completed restructuring transactions are included in the study. The time horizon extends from year 1999 to 2003. A three-year floating window is assigned to examine the sample firms. The key enquiry is to scrutinise the ex post effects of restructuring on performance and value measures of firms with contrast to a matched criteria non-restructured sample. A cross sectional study employing logit estimate is undertaken to examine firm characteristics of restructuring samples. Further, additional parameters, i.e. Conditional Volatility and Asymmetry are generated under the GJR-GARCH estimate and reiterated in logit models to capture time-varying heteroscedasticity of the samples. This research incorporates most forms of restructurings, while prior studies have examined certain forms of restructuring. Particularly, these studies have made limited attempts to examine different restructuring events simultaneously. In addition to logit analysis, an event study is adopted to evaluate the announcement effect of restructuring under both the OLS and GJR-GARCH estimate supplementing our prior results. By engaging a composite empirical framework, our estimation method validates a full appreciation of restructuring effect. The study provides evidence that restructurings indicate non-trivial significant positive effect. There are some evidences that the response differs because of the types of restructuring, particularly while event study is applied. The results establish that performance measures, i.e. Operating Profit Margin, Return on Equity, Return on Assets, Growth, Size, Profit Margin and Shareholders' Ownership indicate consistent and significant increase. However, Leverage and Asset Turn Over suggest reasonable influence on restructuring across the sample period. Similarly, value measures, i.e. Abnormal Returns, Return on Equity and Cash Flow Margin suggest sizeable improvement. A notable characteristic seen coherently throughout the analysis is the decreasing proportion of Systematic Risk. Consistent with these findings, Conditional Volatility and Asymmetry exhibit similar trend. The event study analysis suggests that on an average market perceives restructuring favourably and shareholders experience significant and systematic positive gain.

Wealth effects of mergers and acquisitions for US firms: using alternative pricing models

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

Gaussian process quantile regression using expectation propagation

Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm. We describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets. The method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full Gaussian process probabilistic framework.

The link between HR practices, psychological contract fulfillment, and organizational performance:the case of the Greek services sector

The purpose of this study is to investigate the impact of human resource (HR) practices on organizational performance through the mediating role of psychological contract (expressed by the influence of employer on employee promises fulfillment through employee attitudes). The study is based on a national sample of 78 organizations from the public and private services sector in Greece, including education, health, and banking, and on data obtained from 348 employees. The statistical method employed is structural equation modeling, via LISREL and bootstrapping estimation. The findings of the study suggest that employee incentives, performance appraisal, and employee promotion are three major HR practices that must be extensively employed. Furthermore, the study suggests that the organization must primarily keep its promises about a pleasant and safe working environment, respectful treatment, and feedback for performance, in order for employees to largely keep their own promises about showing loyalty to the organization, maintaining high levels of attendance, and upholding company reputation. Additionally, the study argues that the employee attitudes of motivation, satisfaction, and commitment constitute the nested epicenter mediating construct in both the HR practices–performance and employer–employee promise fulfillment relationships, resulting in superior organizational performance. © 2012 Wiley Periodicals, Inc.

Fiber Bragg grating structures inscribed using 800nm femtosecond laser in single- and multi-core mid-IR glass fibers with their spectral, thermal and strain properties

Single- and multi-core passive and active germanate and tellurite glass fibers represent a new class of fiber host for in-fiber photonics devices and applications in mid-IR wavelength range, which are in increasing demand. Fiber Bragg grating (FBG) structures have been proven as one of the most functional in-fiber devices and have been mass-produced in silicate fibers by UV-inscription for almost countless laser and sensor applications. However, because of the strong UV absorption in germanate and tellurite fibers, FBG structures cannot be produced by UVinscription. In recent years femtosecond (fs) lasers have been developed for laser machining and microstructuring in a variety of glass fibers and planar substrates. A number of papers have been reported on fabrication of FBGs and long-period gratings in optical fibers and also on the photosensitivity mechanism using 800nm fs lasers. In this paper, we demonstrate for the first time the fabrication of FBG structures created in passive and active single- and three-core germanate and tellurite glass fibers by using 800nm fs-inscription and phase mask technique. With a fs peak power intensity in the order of 1011W/cm2, the FBG spectra with 2nd and 3rd order resonances at 1540nm and 1033nm in a single-core germanate glass fiber and 2nd order resonances between ~1694nm and ~1677nm with strengths up to 14dB in all three cores of three-core passive and active tellurite fibers were observed. Thermal and strain properties of the FBGs made in these mid-IR glass fibers were characterized, showing an average temperature responsivity of ~20pm/°C and a strain sensitivity of 1.219±0.003pm/µe.