Structure and palaeoenvironmental implications of inter-branch coenosteum-rich skeleton in corymbose Acropora species

Coral reefs provide an increasingly important archive of palaeoclimate data that can be used to constrain climate model simulations. Reconstructing past environmental conditions may also provide insights into the potential of reef systems to survive changes in the Earth’s climate. Reef-based palaeoclimate reconstructions are predominately derived from colonies of massive Porites, with the most abundant genus in the Indo-Pacific—Acropora—receiving little attention owing to their branching growth trajectories, high extension rates and secondary skeletal thickening. However, inter-branch skeleton (consisting of both coenosteum and corallites) near the bases of corymbose Acropora colonies holds significant potential as a climate archive. This region of Acropora skeleton is atypical, having simple growth trajectories with parallel corallites, approximately horizontal density banding, low apparent extension rates and a simple microstructure with limited secondary thickening. Hence, inter-branch skeleton in Acropora bears more similarities to the coralla of massive corals, such as Porites, than to traditional Acropora branches. Cyclic patterns of Sr/Ca ratios in this structure suggest that the observed density banding is annual in nature, thus opening up the potential to use abundant corymbose Acropora for palaeoclimate reconstruction.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

An improved modal strain energy method for structural damage detection, 2D simulation

Structural damage detection using modal strain energy (MSE) is one of the most efficient and reliable structural health monitoring techniques. However, some of the existing MSE methods have been validated for special types of structures such as beams or steel truss bridges which demands improving the available methods. The purpose of this study is to improve an efficient modal strain energy method to detect and quantify the damage in complex structures at early stage of formation. In this paper, a modal strain energy method was mathematically developed and then numerically applied to a fixed-end beam and a three-story frame including single and multiple damage scenarios in absence and presence of up to five per cent noise. For each damage scenario, all mode shapes and natural frequencies of intact structures and the first five mode shapes of assumed damaged structures were obtained using STRAND7. The derived mode shapes of each intact and damaged structure at any damage scenario were then separately used in the improved formulation using MATLAB to detect the location and quantify the severity of damage as compared to those obtained from previous method. It was found that the improved method is more accurate, efficient and convergent than its predecessors. The outcomes of this study can be safely and inexpensively used for structural health monitoring to minimize the loss of lives and property by identifying the unforeseen structural damages.

Characterization of the sulphate mineral coquimbite, a secondary iron sulphate from Javier Ortega mine, Lucanas Province, Peru – Using infrared, Raman spectroscopy and thermogravimetry

The mineral coquimbite has been analysed using a range of techniques including SEM with EDX, thermal analytical techniques and Raman and infrared spectroscopy. The mineral originated from the Javier Ortega mine, Lucanas Province, Peru. The chemical formula was determined as ðFe3þ 1:37; Al0:63ÞP2:00ðSO4Þ3 9H2O. Thermal analysis showed a total mass loss of 73.4% on heating to 1000 C. A mass loss of 30.43% at 641.4 C is attributed to the loss of SO3. Observed Raman and infrared bands were assigned to the stretching and bending vibrations of sulphate tetrahedra, aluminium oxide/hydroxide octahedra, water molecules and hydroxyl ions. The Raman spectrum shows well resolved bands at 2994, 3176, 3327, 3422 and 3580 cm 1 attributed to water stretching vibrations. Vibrational spectroscopy combined with thermal analysis provides insight into the structure of coquimbite.

Vibrational spectroscopic characterization of the arsenate mineral barahonaite: Implications for the molecular structure

The mineral barahonaite is in all probability a member of the smolianinovite group. The mineral is an arsenate mineral formed as a secondary mineral in the oxidized zone of sulphide deposits. We have studied the barahonaite mineral using a combination of Raman and infrared spectroscopy. The mineral is characterized by a series of Raman bands at 863 cm−1 with low wavenumber shoulders at 802 and 828 cm−1. These bands are assigned to the arsenate and hydrogen arsenate stretching vibrations. The infrared spectrum shows a broad spectral profile. Two Raman bands at 506 and 529 cm−1 are assigned to the triply degenerate arsenate bending vibration (F 2, ν4), and the Raman bands at 325, 360, and 399 cm−1 are attributed to the arsenate ν2 bending vibration. Raman and infrared bands in the 2500–3800 cm−1 spectral range are assigned to water and hydroxyl stretching vibrations. The application of Raman spectroscopy to study the structure of barahonaite is better than infrared spectroscopy, probably because of the much higher spatial resolution.

The effect of graphene oxide and its oxidized debris on the cure chemistry and interphase structure of epoxy nanocomposites

The influence of graphene oxide (GO) and its surface oxidized debris (OD) on the cure chemistry of an amine cured epoxy resin has been investigated by Fourier Transform Infrared Emission Spectroscopy (FT-IES) and Differential Scanning Calorimetry (DSC). Spectral analysis of IR radiation emitted at the cure temperature from thin films of diglycidyl ether of bisphenol A epoxy resin (DGEBA) and 4,4'-diaminodiphenylmethane (DDM) curing agent with and without GO allowed the cure kinetics of the interphase between the bulk resin and GO to be monitored in real time, by measuring both the consumption of primary (1°) amine and epoxy groups, formation of ether groups as well as computing the profiles for formation of secondary (2°) and tertiary (3°) amines. OD was isolated from as-produced GO (aGO) by a simple autoclave method to give OD-free autoclaved GO (acGO). It has been found that the presence of OD on the GO prevents active sites on GO surfaces fully catalysing and participating in the reaction of DGEBA with DDM, which results in slower reaction and a lower crosslink density of the three-dimensional networks in the aGO-resin interphase compared to the acGO-resin interphase. We also determined that OD itself promoted DGEBA homopolymerization. A DSC study further confirmed that the aGO nanocomposite exhibited lower Tg while acGO nanocomposite showed higher Tg compared to neat resin because of the difference in crosslink densities of the matrix around the different GOs.

Structure and vascular tissue expression of duplicated TERMINAL EAR1-like paralogues in poplar

TERMINAL EAR1-like (TEL) genes encode putative RNA-binding proteins only found in land plants. Previous studies suggested that they may regulate tissue and organ initiation in Poaceae. Two TEL genes were identified in both Populus trichocarpa and the hybrid aspen Populus tremula × P. alba, named, respectively, PoptrTEL1-2 and PtaTEL1-2. The analysis of the organisation around the PoptrTEL genes in the P. trichocarpa genome and the estimation of the synonymous substitution rate for PtaTEL1-2 genes indicate that the paralogous link between these two Populus TEL genes probably results from the Salicoid large-scale gene-duplication event. Phylogenetic analyses confirmed their orthology link with the other TEL genes. The expression pattern of both PtaTEL genes appeared to be restricted to the mother cells of the plant body: leaf founder cells, leaf primordia, axillary buds and root differentiating tissues, as well as to mother cells of vascular tissues. Most interestingly, PtaTEL1-2 transcripts were found in differentiating cells of secondary xylem and phloem, but probably not in the cambium itself. Taken together, these results indicate specific expression of the TEL genes in differentiating cells controlling tissue and organ development in Populus (and other Angiosperm species).

Detection of anuran calling activity in long field recordings for bio-acoustic monitoring

This paper presents a system to analyze long field recordings with low signal-to-noise ratio (SNR) for bio-acoustic monitoring. A method based on spectral peak track, Shannon entropy, harmonic structure and oscillation structure is proposed to automatically detect anuran (frog) calling activity. Gaussian mixture model (GMM) is introduced for modelling those features. Four anuran species widespread in Queensland, Australia, are selected to evaluate the proposed system. A visualization method based on extracted indices is employed for detection of anuran calling activity which achieves high accuracy.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.