Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation

A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems

A new class of multiscale lattice cell (MLC) models for spatio-temporal evolutionary image representation

Spatio-temporal evolutionary (STE) images are a class of complex dynamical systems that evolve over both space and time. With increased interest in the investigation of nonlinear complex phenomena, especially spatio-temporal behaviour governed by evolutionary laws that are dependent on both spatial and temporal dimensions, there has been an increased need to investigate model identification methods for this class of complex systems. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite challenging. Starting with an assumption that there is no apriori information about the true model but only observed data are available, this study introduces a new class of multiscale lattice cell (MLC) models to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, is investigated to demonstrate the new modelling framework

Detecting and tracking time-varying causality with applications to EEG data

This paper introduces a novel method called the ERR-Causality, or Error Reduction Ratio Causality test, that can be used to detect and track causal relationships between two signals using a new adaptive forward orthogonal least squares (Adaptive-Forward-OLS) algorithm. In comparison to the traditional Granger method, one advantage of the new ERR-Causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-Causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Several numerical examples are provided to illustrate the effectiveness of the new method for causal relationship detection between two signals. An important real application, relating to the analysis of the causality of EEG signals from different cortical sites which can be very useful for understanding brain activity during an epileptic seizure by inspecting the high-resolution time varying directed information flow, is also discussed

Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework

Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework

Unselective regrowth of 1.5-μm InGaAsP multiple-quantum-well distributed-feedback buried heterostructure lasers

Unselective regrowth for fabricating 1.5-μm InGaAsP multiple-quantum well (MQW) distributed-feedback (DFB) buried heterostructure (BH) lasers is developed. The experimental results exhibit superior characteristics, such as a low threshold of 8.5mA, high slope efficiency of 0.55mW∕mA, circular-like far-field patterns, the narrow linewidth of 2.5MHz, etc. The high performance of the devices effectively proves the feasibility of the new method to fabricate buried heterostructure lasers

Effect of substituting guinea grass with sunflower hulls on production performance and digestion traits in fattening rabbits

[EN] The objective of this study was to evaluate the use of sunflower hulls (SH) to substitute guinea grass (GG), traditionally used as a fibre source in the diets of fattening rabbits, on production performance, coefficients of total tract apparent digestibility (CTTAD) of nutrients, gastrointestinal tract development and caecal fermentation. A total of 160 mixed sex Hyla commercial meat rabbits were allocated to 4 experimental groups (40 per treatment) differing in the SH level inclusion in the diet offered to rabbits from 40 to 90 d of age: 0, 30, 60 and 90 g/kg on as-fed basis: SH0, SH30, SH60 and SH90 groups, respectively. Growth performance was recorded from 47 to 90 d of age, CTTAD of nutrients from 86 to 90 d of age, and gastrointestinal tract development, caecal fermentation and carcass traits were determined at 90 d of age. Increasing substitutions of SH in the diet indicated effects on growth performance, as higher feed intake and lower feed efficiency were observed in SH90 compared with SH0 (P-linear<0.05). Moreover, the higher SH substitution diet (SH60 and SH90) increased the relative caecum weight (P-linear<0.05). A linear negative effect of SH inclusion was observed for the digestibility of neutral detergent fibre (CTTAD from 0.294 to 0.232) and acid detergent fibre (CTTAD from 0.182 to 0.136; P-linear<0.05). Dietary SH substitution level had a quadratic effect on the villus height of the duodenum, jejunum and ileum obtained (P-quadratic<0.05), and the highest were observed in the SH60 group. There was a quadratic effect on the pH of caecum content (P-quadratic<0.05), and the lowest was 6.08 in SH30 group. The total volatil fatty acids increased linearly with increasing SH in diets (from 71.11 to 76.98 mmol/L; P-linear<0.05), and when dietary SH increased, the proportion of acetate tended to increase (P-linear<0.05), and the proportions of propionic and butyric were decreased (P-linear<0.05, respectively). Substitution of GG with SH had no effect on carcass characteristics and meat quality. The current work shows that SH can replace up to 60 g/kg in diets for fattening rabbits, with no adverse impact on aspects of production performance or digestion traits.This study was supported by the earmarked fund for Modern Agro-industry Technology Research System (CARS-43-B-1) and Funds of Shandong “Double Tops” Programme.Liu, G.; Sun, C.; Zhao, X.; Liu, H.; Wu, Z.; Li, F. (2018). Effect of substituting guinea grass with sunflower hulls on production performance and digestion traits in fattening rabbits. World Rabbit Science. 26(3):217-225. https://doi.org/10.4995/wrs.2018.9375SWORD21722526

Differential regulation of effector- and central-memory responses to Toxoplasma gondii infection by IL-12 revealed by tracking of Tgd057-specific CD8+ T cells

10.1371/journal.ppat.1000815PLoS Pathogens6

Investigating an unusually large 28-day oscillation in mesospheric temperature over Antarctica using ground-based and satellite measurements

The Utah State University (USU) Advanced Mesospheric Temperature Mapper (AMTM) was deployed at the Amundsen‐Scott South Pole Station in 2010 to measure OH temperature at ~87 km as part of an international network to study the mesospheric dynamics over Antarctica. During the austral winter of 2014, an unusually large amplitude ~28‐day oscillation in mesospheric temperature was observed for ~100 days from the South Pole Station. This study investigates the characteristics and global structure of this exceptional planetary‐scale wave event utilizing ground‐based mesospheric OH temperature measurements from two Antarctic stations (South Pole and Rothera) together with satellite temperature measurements from the Microwave Limb Sounder (MLS) on the Aura satellite, and the Solar Occultation For Ice Experiment (SOFIE) on the Aeronomy of Ice in the Mesosphere (AIM) satellite. Our analyses have revealed that this large oscillation is a winter time, high latitude phenomenon, exhibiting a coherent zonal wave #1 structure below 80 km altitude. At higher altitudes, the wave was confined in longitude between 180‐360°E. The amplitude of this oscillation reached ~15 K at 85 km and it was observed to grow with altitude as it extended from the stratosphere into the lower thermosphere in the southern hemisphere. The satellite data further established the existence of this oscillation in the northern hemisphere during the boreal winter time. The main characteristics and global structure of this event as observed in temperature are consistent with the predicted 28‐day Rossby Wave (1,4) mode