Estimating present day extreme total water level exceedance probabilities around the Australian coastline

The occurrence of extreme water level events along low-lying, highly populated and/or developed coastlines can lead to devastating impacts on coastal infrastructure. Therefore it is very important that the probabilities of extreme water levels are accurately evaluated to inform flood and coastal management and for future planning. The aim of this study was to provide estimates of present day extreme total water level exceedance probabilities around the whole coastline of Australia, arising from combinations of mean sea level, astronomical tide and storm surges generated by both extra-tropical and tropical storms, but exclusive of surface gravity waves. The study has been undertaken in two main stages. In the first stage, a high-resolution (~10 km along the coast) hydrodynamic depth averaged model has been configured for the whole coastline of Australia using the Danish Hydraulics Institute’s Mike21 modelling suite of tools. The model has been forced with astronomical tidal levels, derived from the TPX07.2 global tidal model, and meteorological fields, from the US National Center for Environmental Prediction’s global reanalysis, to generate a 61-year (1949 to 2009) hindcast of water levels. This model output has been validated against measurements from 30 tide gauge sites around Australia with long records. At each of the model grid points located around the coast, time series of annual maxima and the several highest water levels for each year were derived from the multi-decadal water level hindcast and have been fitted to extreme value distributions to estimate exceedance probabilities. Stage 1 provided a reliable estimate of the present day total water level exceedance probabilities around southern Australia, which is mainly impacted by extra-tropical storms. However, as the meteorological fields used to force the hydrodynamic model only weakly include the effects of tropical cyclones the resultant water levels exceedance probabilities were underestimated around western, northern and north-eastern Australia at higher return periods. Even if the resolution of the meteorological forcing was adequate to represent tropical cyclone-induced surges, multi-decadal periods yielded insufficient instances of tropical cyclones to enable the use of traditional extreme value extrapolation techniques. Therefore, in the second stage of the study, a statistical model of tropical cyclone tracks and central pressures was developed using histroic observations. This model was then used to generate synthetic events that represented 10,000 years of cyclone activity for the Australia region, with characteristics based on the observed tropical cyclones over the last ~40 years. Wind and pressure fields, derived from these synthetic events using analytical profile models, were used to drive the hydrodynamic model to predict the associated storm surge response. A random time period was chosen, during the tropical cyclone season, and astronomical tidal forcing for this period was included to account for non-linear interactions between the tidal and surge components. For each model grid point around the coast, annual maximum total levels for these synthetic events were calculated and these were used to estimate exceedance probabilities. The exceedance probabilities from stages 1 and 2 were then combined to provide a single estimate of present day extreme water level probabilities around the whole coastline of Australia.

A benzothiadiazole end capped donor–acceptor based small molecule for organic electronics

A benzothiadiazole end-capped small molecule 3,6-bis(5-(benzo[c][1,2,5] thiadiazol-4-yl)thiophen-2-yl)-2,5-bis(2-butyloctyl)pyrrolo[3,4-c]pyrrole-1, 4(2H,5H)-dione (BO-DPP-BTZ) using a fused aromatic moiety DPP (at the centre) is designed and synthesized. BO-DPP-BTZ is a donor-acceptor-donor (D-A-D) structure which possesses a band gap of 1.6 eV and exhibits a strong solid state ordering inferred from ∼120 nm red shift of the absorption maxima from solution to thin film. Field-effect transistors utilizing a spin coated thin film of BO-DPP-BTZ as an active layer exhibited a hole mobility of 0.06 cm 2 V-1 s-1. Solution-processed bulk heterojunction organic photovoltaics employing a blend of BO-DPP-BTZ and [70]PCBM demonstrated a power conversion efficiency of 0.9%.

Furan substituted diketopyrrolopyrrole and thienylenevinylene based low band gap copolymer for high mobility organic thin film transistors

A novel solution processable donor-acceptor (D-A) based low band gap polymer semiconductor poly{3,6-difuran-2-yl-2,5-di(2-octyldodecyl)-pyrrolo[3,4- c]pyrrole-1,4-dione-alt-thienylenevinylene} (PDPPF-TVT), was designed and synthesized by a Pd-catalyzed Stille coupling route. An electron deficient furan based diketopyrrolopyrrole (DPP) block and electron rich thienylenevinylene (TVT) donor moiety were attached alternately in the polymer backbone. The polymer exhibited good solubility, film forming ability and thermal stability. The polymer exhibits wide absorption bands from 400 nm to 950 nm (UV-vis-NIR region) with absorption maximum centered at 782 nm in thin film. The optical band gap (Eoptg) calculated from the polymer film absorption onset is around 1.37 eV. The π-energy band level (ionization potential) calculated by photoelectron spectroscopy in air (PESA) for PDPPF-TVT is around 5.22 eV. AFM and TEM analyses of the polymer reveal nodular terrace morphology with optimized crystallinity after 200 °C thermal annealing. This polymer exhibits p-channel charge transport characteristics when used as the active semiconductor in organic thin-film transistor (OTFT) devices. The highest hole mobility of 0.13 cm 2 V -1 s -1 is achieved in bottom gate and top-contact OTFT devices with on/off ratios in the range of 10 6-10 7. This work reveals that the replacement of thiophene by furan in DPP copolymers exhibits such a high mobility, which makes DPP furan a promising block for making a wide range of promising polymer semiconductors for broad applications in organic electronics.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

The use of penumbrae to define and analyse non-standard fields

Aim A new method of penumbral analysis is implemented which allows an unambiguous determination of field size and penumbra size and quality for small fields and other non-standard fields. Both source occlusion and lateral electronic disequilibrium will affect the size and shape of cross-axis profile penumbrae; each is examined in detail. Method A new method of penumbral analysis is implemented where the square of the derivative of the cross-axis profile is plotted. The resultant graph displays two peaks in the place of the two penumbrae. This allows a strong visualisation of the quality of a field penumbra, as well as a mathematically consistent method of determining field size (distance between the two peak’s maxima), and penumbra (full-widthtenth-maximum of peak). Cross-axis profiles were simulated in a water phantom at a depth of 5 cm using Monte Carlo modelling, for field sizes between 5 and 30 mm. The field size and penumbra size of each field was calculated using the method above, as well as traditional definitions set out in IEC976. The effect of source occlusion and lateral electronic disequilibrium on the penumbrae was isolated by repeating the simulations removing electron transport and using an electron spot size of 0 mm, respectively. Results All field sizes calculated using the traditional and proposed methods agreed within 0.2 mm. The penumbra size measured using the proposed method was systematically 1.8 mm larger than the traditional method at all field sizes. The size of the source had a larger effect on the size of the penumbra than did lateral electronic disequilibrium, particularly at very small field sizes. Conclusion Traditional methods of calculating field size and penumbra are proved to be mathematically adequate for small fields. However, the field size definition proposed in this study would be more robust amongst other nonstandard fields, such as flattening filter free. Source occlusion plays a bigger role than lateral electronic disequilibrium in small field penumbra size.

Multiscale features and information extraction of online strain for long-span bridges

The strain data acquired from structural health monitoring (SHM) systems play an important role in the state monitoring and damage identification of bridges. Due to the environmental complexity of civil structures, a better understanding of the actual strain data will help filling the gap between theoretical/laboratorial results and practical application. In the study, the multi-scale features of strain response are first revealed after abundant investigations on the actual data from two typical long-span bridges. Results show that, strain types at the three typical temporal scales of 10^5, 10^2 and 10^0 sec are caused by temperature change, trains and heavy trucks, and have their respective cut-off frequency in the order of 10^-2, 10^-1 and 10^0 Hz. Multi-resolution analysis and wavelet shrinkage are applied for separating and extracting these strain types. During the above process, two methods for determining thresholds are introduced. The excellent ability of wavelet transform on simultaneously time-frequency analysis leads to an effective information extraction. After extraction, the strain data will be compressed at an attractive ratio. This research may contribute to a further understanding of actual strain data of long-span bridges; also, the proposed extracting methodology is applicable on actual SHM systems.

Running shoes increase Achilles tendon load in walking : an acoustic propagation study

Background: Footwear remains a prime candidate for the prevention and rehabilitation of Achilles tendinopathy as it is thought to decrease tension in the tendon through elevation of the heel. However, evidence for this effect is equivocal. Purpose: This study used an acoustic transmission technique to investigate the effect of running shoes on Achilles tendon loading during barefoot and shod walking. Methods: Acoustic velocity was measured in the Achilles tendon of twelve recreationally–active males (age, 31±9 years; height, 1.78±0.06 m; weight, 81.0±16.9 kg) during barefoot and shod walking at matched self–selected speed (3.4±0.7 km/h). Standard running shoes incorporating a 10– mm heel offset were used. Vertical ground reaction force and spatiotemporal parameters were determined with an instrumented treadmill. Axial acoustic velocity in the Achilles tendon was measured using a custom built ultrasonic device. All data were acquired at a rate of 100 Hz during 10s of steady–state walking. Statistical comparisons between barefoot and shod conditions were made using paired t–tests and repeated measure ANOVAs. Results: Acoustic velocity in the Achilles tendon was highly reproducible and was typified by two maxima (P1, P2) and minima (M1, M2) during walking. Footwear resulted in a significant increase in step length, stance duration and peak vertical ground reaction force compared to barefoot walking. Peak acoustic velocity in the Achilles tendon (P1, P2) was significantly higher with running shoes. Conclusions: Peak acoustic velocity in the Achilles tendon was higher with footwear, suggesting that standard running shoes with a 10–mm heel offset increase tensile load in the Achilles tendon. Although further research is required, these findings question the therapeutic role of standard running shoes in Achilles tendinopathy.

Computation of Mathematical Models for Complex Industrial Processes

Designed for undergraduate and postgraduate students, academic researchers and industrial practitioners, this book provides comprehensive case studies on numerical computing of industrial processes and step-by-step procedures for conducting industrial computing. It assumes minimal knowledge in numerical computing and computer programming, making it easy to read, understand and follow. Topics discussed include fundamentals of industrial computing, finite difference methods, the Wavelet-Collocation Method, the Wavelet-Galerkin Method, High Resolution Methods, and comparative studies of various methods. These are discussed using examples of carefully selected models from real processes of industrial significance. The step-by-step procedures in all these case studies can be easily applied to other industrial processes without a need for major changes and thus provide readers with useful frameworks for the applications of engineering computing in fundamental research problems and practical development scenarios.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Pt(II) bipyridyl complexes bearing substituted fluorenyl motif on the bipyridyl and acetylide ligands : synthesis, photophysics, and reverse saturable absorption

A series of Pt(II) diimine complexes bearing benzothiazolylfluorenyl (BTZ-F8), diphenylaminofluorenyl (NPh2- F8), or naphthalimidylfluorenyl (NI-F8) motifs on the bipyridyl or acetylide ligands (Pt-4−Pt-8), (i.e., {4,4′-bis[7-R1-F8-(≡)n-]bpy}Pt(7- R2-F8- ≡ -)2, where F8 = 9,9′-di(2-ethylhexyl)fluorene, bpy = 2,2′- bipyridine, Pt-4: R1 = R2 = BTZ, n = 0; Pt-5: R1 = BTZ, R2 = NI, n = 0; Pt-6: R1 = R2 = BTZ, n = 1; Pt-7: R1 = BTZ, R2 = NPh2, n = 1; Pt- 8: R1 = NPh2, R2 = BTZ, n = 1) were synthesized. Their ground-state and excited-state properties and reverse saturable absorption performances were systematically investigated. The influence of these motifs on the photophysics of the complexes was investigated by spectroscopic methods and simulated by time-dependent density functional theory (TDDFT). The intense absorption bands below 410 nm for these complexes is assigned to predominantly 1π,π* transitions localized on either the bipyridine or the acetylide ligands; while the broad low-energy absorption bands between 420 and 575 nm are attributed to essentially 1MLCT (metal-to-ligand charge transfer)/ 1LLCT (ligand-to-ligand charge transfer) transitions, likely mixed with some 1ILCT (intraligand charge transfer) transition for Pt-4−Pt-7, and predominantly 1ILCT transition admixing with minor 1MLCT/1LLCT characters for Pt-8. The different substituents on the acetylide and bipyridyl ligands, and the degrees of π-conjugation in the bipyridyl ligand influence both the 1π,π* and charge transfer transitions pronouncedly. All complexes are emissive at room temperature. Upon excitation at their respective absorption band maxima, Pt-4, Pt-6, and Pt-8 exhibit acetylide ligand localized 1π,π* fluorescence and 3MLCT/3LLCT phosphorescence in CH2Cl2, while Pt-5 manifests 1ILCT fluorescence and 3ILCT phosphorescence. However, only 1LLCT fluorescence was observed for Pt-7 at room temperature. The nanosecond transient absorption study was carried out for Pt-4−Pt-8 in CH3CN. Except for Pt-7 that contains NPh2 at the acetylide ligands, Pt-4−Pt-6 and Pt-8 all exhibit weak to moderate excited-state absorption in the visible spectral region. Reverse saturable absorption (RSA) of these complexes was demonstrated at 532 nm using 4.1 ns laser pulses in a 2 mm cuvette. The strength of RSA follows this trend: Pt-4 > Pt-5 > Pt-7 > Pt-6 > Pt-8. Incorporation of electron-donating substituent NPh2 on the bipyridyl ligand significantly decreases the RSA, while shorter π-conjugation in the bipyridyl ligand increases the RSA. Therefore, the substituent at either the acetylide ligands or the bipyridyl ligand could affect the singlet and triplet excited-state characteristics significantly, which strongly influences the RSA efficiency.

Computational analysis of epigenetic information in human DNA sequences

Over the last few years, investigations of human epigenetic profiles have identified key elements of change to be Histone Modifications, stable and heritable DNA methylation and Chromatin remodeling. These factors determine gene expression levels and characterise conditions leading to disease. In order to extract information embedded in long DNA sequences, data mining and pattern recognition tools are widely used, but efforts have been limited to date with respect to analyzing epigenetic changes, and their role as catalysts in disease onset. Useful insight, however, can be gained by investigation of associated dinucleotide distributions. The focus of this paper is to explore specific dinucleotides frequencies across defined regions within the human genome, and to identify new patterns between epigenetic mechanisms and DNA content. Signal processing methods, including Fourier and Wavelet Transformations, are employed and principal results are reported.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.