QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS

We study inference and diagnostics for count time series regression models that include a feedback mechanism. In particular, we are interested in negative binomial processes for count time series. We study probabilistic properties and quasi‐likelihood estimation for this class of processes. We show that the resulting estimators are consistent and asymptotically normally distributed. These facts enable us to construct probability integral transformation plots for assessing any assumed distributional assumptions. The key observation in developing the theory is a mean parameterized form of the negative binomial distribution. For transactions data, it is seen that the negative binomial distribution offers a better fit than the Poisson distribution. This is an immediate consequence of the fact that transactions can be represented as a collection of individual activities that correspond to different trading strategies.     

Simulating Future Groundwater Recharge in Coastal and Inland Catchments

Groundwater is a primary source of drinking water in the Mediterranean, however, climate variability in conjunction with mismanagement renders it vulnerable to depletion. Spatiotemporal studies of groundwater recharge are the basis to develop strategies against this phenomenon. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in one coastal and one inland hydrological basin in Greece. A double calibration/validation (CV) procedure using streamflow data and MODIS ET was conducted for the inland basin of Mouriki, whereas only ET values were used in the coastal basin of Anthemountas. Calibration and simulation recharge were accurate in both sites according to statistical indicators and previous studies. In Mouriki basin, mean recharge and runoff were estimated as 16% and 9%, respectively. In Anthemountas basin recharge to the shallow aquifer and surface runoff were estimated as 12% and 16%, respectively. According to the predicted RCP 4.5 and 8.5 scenarios, significant variations in groundwater recharge are predicted in the coastal zone for the period 2020–2040 with average annual recharges decreasing by 30% (RCP 4.5) and 25% (RCP 8.5). Variations in groundwater recharge in the inland catchment of Mouriki were insignificant for the simulated period. Anthemountas basin was characterized by higher runoff rates. Groundwater management in coastal aquifers should include detailed monitoring of hydrological parameters, reinforced groundwater recharge during winter and reduced groundwater abstraction during summer depending on the spatiotemporal distribution of groundwater recharge.

     

Parallel cooperative micro-particle swarm optimization: A master–slave model

A parallel master–slave model of the recently proposed cooperative micro-particle swarm optimization approach is introduced. The algorithm is based on the decomposition of the original search space in subspaces of smaller dimension. Each subspace is probed by a subswarm of small size that identifies suboptimal partial solution components. A context vector that serves as repository for the best attained partial solutions of all subswarms is used for the evaluation of the particles. The required modifications to fit the original algorithm within a parallel computation framework are discussed along with their impact on performance. Also, both linear and random allocation of direction components to subswarms are considered to render the algorithm capable of capturing possible correlations among decision variables. The proposed approach is evaluated on two types of computer systems, namely an academic cluster and a desktop multicore system, using a popular test suite. Statistical analysis of the obtained results reveals that, besides the expected run-time superiority of the parallel model, significant improvements in solution quality can also be achieved. Different factors that may affect performance are pointed out, offering intuition on the expected behavior of the parallel model.     

Improving the Design of Greek Hollow Clay Bricks

The hollow clay brick is the typical building unit that is employed not only over the whole Greece but also in many other Mediterranean countries. Nevertheless, its design is completely empirical. In this study, the design of the hollow clay brick is analyzed by employing a finite element package. To carry out this analysis, the thermal conductivity of the solid clay is measured by the transient hot-wire technique. As a consequence of the analysis, an improvement of 24 % in the design of the hollow clay brick is proposed.     

Finding all roots of 2?×?2 nonlinear algebraic systems using back-propagation neural networks

The objective of this research is the numerical estimation of the roots of a complete 2?×?2 nonlinear algebraic system of polynomial equations using a feed forward back-propagation neural network. The main advantage of this approach is the simple solution of the system, by building a structure—including product units—that simulates exactly the nonlinear system under consideration and find its roots via the classical back-propagation approach. Examples of systems with four or multiple roots were used, in order to test the speed of convergence and the accuracy of the training algorithm. Experimental results produced by the network were compared with their theoretical values.     

FE parametric study of RWS/WUF-B moment connections with elliptically-based beam web openings under monotonic and cyclic loading

International Journal of Steel Structures - This paper provides numerical results investigating the behaviour of steel web-perforated beams with different shaped single openings located close to...     

Development of an Easy Multiresidue Method for Fat-Soluble Pesticides in Animal Products Using Gas Chromatography–Tandem Mass Spectrometry

The determination of pesticide residues in animal samples requires several analytical steps which may be time-consuming extraction procedures and with a small scope. For fat-soluble pesticides, the determination of fat is essential as they tend to bioaccumulate in fat. As to address these concerns, an easy acetonitrile-based multiresidue method combined with GC-MS-MS technology for the determination of the fat-soluble analytes was developed and validated on animal products. Analytes from several chemical classes such as diphenyl ethers, pyrethroids, organochlorides, triazoles, carbamide, chlorophenyls, dinitroaniline, organophosphorus, chloroacetamides, benzamides, aromatic hydrocarbon and dicarboximide known, due to their fat-soluble property, to bioaccumulate in animal tissues were selected. The method showed acceptable linearity (r?≥?0.99) and accuracy with recoveries between 70 and 120 % and precision with SD_R?≤?20 % for the majority of the analytes studied. For the analytes that presented accuracy and precision values outside the acceptable limits, the method still is able to serve as a semi-quantitative method. The contribution of the determination of the fat in the results was also investigated. The limit of detection was set at 0.003 mg/kg. The proposed methodology was applied to meat and offal samples from the regional market in Attiki (Greece).     

Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues,databases, and tools

While detecting and interpreting temporal patterns of nonverbal behavioural cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human‐centred interaction they are bound to experience. This paper surveys the nonverbal behavioural cues that could be present during displays of agreement and disagreement; discusses a number of methods that could be used or adapted to detect these suggested cues; lists some publicly available databases these tools could be trained on for the analysis of spontaneous, audiovisual instances of agreement and disagreement, it examines the few existing attempts at agreement and disagreement classification, and finally discusses the challenges in automatically detecting agreement and disagreement.