Soil–pile interaction of pile embedded in deep-layered marine sediment under seismic excitation

This paper investigates the soil–pile interaction of a pile embedded in a deep multi-layered soil under seismic excitation considering both kinematic and inertial interaction effects. A comprehensive three-dimensional finite element model is developed and validated using existing results in the literature. The response of the pile in the deep multi-layered soil profile is investigated with respect to pile head response, deflection modes and maximum deflections along the pile. Results show that the pile exhibits complex deflection patterns and that the pile response is influenced by the properties of both the soil profile and the seismic excitation. It is also evident that kinematic interaction effects have a greater influence on the pile response than the inertial interaction effects.

A multimodal physiotherapy programme plus deep water running for improving cancer-related fatigue and quality of life in breast cancer survivors

The aim of the study was to assess the feasibility and effectiveness of aquatic‐based exercise in the form of deep water running ( DWR ) as part of a multimodal physiotherapy programme ( MMPP ) for breast cancer survivors. A controlled clinical trial was conducted in 42 primary breast cancer survivors recruited from community‐based P rimary C are C entres. Patients in the experimental group received a MMPP incorporating DWR , 3 times a week, for an 8‐week period. The control group received a leaflet containing instructions to continue with normal activities. Statistically significant improvements and intergroup effect size were found for the experimental group for P iper F atigue S cale‐ R evised total score ( d = 0.7, P = 0.001), as well as behavioural/severity ( d = 0.6, P = 0.05), affective/meaning ( d = 1.0, P = 0.001) and sensory ( d = 0.3, P = 0.03) domains. Statistically significant differences between the experimental and control groups were also found for general health ( d = 0.5, P < 0.05) and quality of life ( d = 1.3, P < 0.05). All participants attended over 80% of sessions, with no major adverse events reported. The results of this study suggest MMPP incorporating DWR decreases cancer‐related fatigue and improves general health and quality of life in breast cancer survivors. Further, the high level of adherence and lack of adverse events indicate such a programme is safe and feasible.

Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space

Deep geothermal from the hot crystalline basement has remained an unsolved frontier for the geothermal industry for the past 30 years. This poses the challenge for developing a new unconventional geomechanics approach to stimulate such reservoirs. While a number of new unconventional brittle techniques are still available to improve stimulation on short time scales, the astonishing richness of failure modes of longer time scales in hot rocks has so far been overlooked. These failure modes represent a series of microscopic processes: brittle microfracturing prevails at low temperatures and fairly high deviatoric stresses, while upon increasing temperature and decreasing applied stress or longer time scales, the failure modes switch to transgranular and intergranular creep fractures. Accordingly, fluids play an active role and create their own pathways through facilitating shear localization by a process of time-dependent dissolution and precipitation creep, rather than being a passive constituent by simply following brittle fractures that are generated inside a shear zone caused by other localization mechanisms. We lay out a new theoretical approach for the design of new strategies to utilize, enhance and maintain the natural permeability in the deeper and hotter domain of geothermal reservoirs. The advantage of the approach is that, rather than engineering an entirely new EGS reservoir, we acknowledge a suite of creep-assisted geological processes that are driven by the current tectonic stress field. Such processes are particularly supported by higher temperatures potentially allowing in the future to target commercially viable combinations of temperatures and flow rates.

Class participation as a learning and assessment strategy in law : facilitating students’ engagement, skills development and deep learning

Assessment has widely been described as being ‘at the centre of the student experience’. It would be difficult to conceive of the modern teaching university without it. Assessment is accepted as one of the most important tools that an educator can deploy to influence both what and how students learn. Evidence suggests that how students allocate time and effort to tasks and to developing an understanding of the syllabus is affected by the method of assessment utilised and the weighting it is given. This is particularly significant in law schools where law students may be more preoccupied with achieving high grades in all courses than their counterparts from other disciplines. However, well-designed assessment can be seen as more than this. It can be a vehicle for encouraging students to learn and engage more broadly than with the minimums required to complete the assessment activity. In that sense assessment need not merely ‘drive’ learning, but can instead act as a catalyst for further learning beyond what a student had anticipated. In this article we reconsider the potential roles and benefits in legal education of a form of interactive classroom learning we term assessable class participation (‘ACP’), both as part of a pedagogy grounded in assessment and learning theory, and as a platform for developing broader autonomous approaches to learning amongst students. We also consider some of the barriers students can face in ACP and the ways in which teacher approaches to ACP can positively affect the socio-emotional climates in classrooms and thus reduce those barriers. We argue that the way in which a teacher facilitates ACP is critical to the ability to develop positive emotional and learning outcomes for law students, and for teachers themselves.

Single voxel Magnetic Resonance Spectroscopic (1H-MRS) analysis of deep brain structures after traumatic prefrontal brain injury (TBI) in rat

Chronic difficulties arising from mild brain injury (TBI) are difficult to predict because the processes underlying changes after TBI are poorly understood. In mild brain injury the extent of neuropsychiatric and cognitive symptoms correspond poorly to overt tissue loss (Barth 1983; Liu 2010). Cellular, immune and hormonal cascades occurring after injury and continuing during the healing process may impact uninjured brain regions sensitive to the effects of physiological and emotional stress, which receive projections from the injury site. Changes in these most basic properties due to injury or disease have profound implications for virtually every aspect of brain function through disruption of neurotransmitter, neuroendocrine and metabolic systems. In order to screen for changes in transmitter and metabolic activity, in this study we developed Single voxel proton Magnetic Resonance Spectroscopy (1H-MRS) for use in both injured and control animals. We first evaluated if 1H-MRS could be used to evaluate in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus in both control and injured animals after controlled cortical impact injury to the rat prefrontal cortex. We found that metabolite measurements for Myo-Inositol, Choline, creatine, Glutamate+Glutamine, and N-acetyl-acetate are attainable in deep brain structures in vivo in injured and controls rats. We next seek to evaluate longitudinally, in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus during the first month after controlled cortical impact injury to the rat prefrontal cortex. These ongoing studies will provide data on the changes in transmitters and metabolites over time in injured and non-injured subjects. These studies address some of the fundamental questions about how mild brain injury has such diverse effects on overall brain health and function.

Not just what they want, but why they want it: Traditional market research to deep customer insights

Purpose This paper explores advantages and disadvantages of both traditional market research and deep customer insight methods in order to lay the platform for revealing how a relationship between these two domains could be optimised during firm-based innovation. Design/methodology/approach The paper reports on an empirical research study conducted with thirteen Australian based firms engaged in a design-led approach to innovation. Firms were facilitated through a design-led approach where the process of gathering deep customer insights was isolated and investigated further in comparison to traditional market research methods. Findings Results show that deep customer insight methods are able to provide fresh, non-obvious ways of understanding customer needs, problems and behaviours that can become the foundation of new business opportunities. Findings concluded that deep customer insights methods provide the critical layer to understand why customers do and don’t engage with businesses. Revealing why was not accessible in traditional market research methods. Research limitations/implications The theoretical outcome of this study is a complementary methods matrix, providing guidance on appropriate implementation of research methods in accordance with a project’s timeline to optimise the complementation of traditional market research methods with design-led customer engagement methods. Practical implications Deep customer insight methods provide fresh, non-obvious ways of understanding customer needs, problems and behaviours that can become the foundation of new business opportunities. It is hoped that those in a position of data collection are encouraged to experiment and use deep customer insight methods to connect with their customers on a meaningful level and translate these insights into value. Originality/value This paper provides original value to a new understanding how design techniques can be applied to compliment and strengthen existing market research strategies. This is crucial in an era where business competition hinges on a subtle and often intimate understanding of customer needs and behaviours.

Impacts of sodic soil amelioration on hydraulic conductivity and deep drainage in the Lower Burdekin

An understanding of the influence of soil chemistry on soil hydraulic properties is of critical importance for the management of sodic soils under irrigation. The hydraulic conductivity of sodic soils has been shown to be affected by properties of the applied solution including pH (Suarez et al. 1984), sodicity and salt concentration (McNeal and Coleman 1966). The changes in soil hydraulic conductivity are the result of changes in the spacing between clay layers in response to changes in soil solution chemistry. While the importance o f soil chemistry in controlling hydraulic conductivity is known, the exact impacts of sodic soil amelioration on hydraulic conductivity and deep drainage at a given location are difficult to predict. This is because the relationships between soil chemical factors and hydraulic conductivity are soil specific and because local site specific factors also need to be considered to determine the actual impacts on deep drainage rates.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Solvent effect in molecular recognition: Determining binding constants in different solvents following an extraction based protocol

Semi-rigid molecular tweezers 1, 3 and 4 bind picric acid with more than tenfold increment in tetrachloromethane as compared to chloroform.

Design and a deep customer insight approach to innovation

This paper proposes and explores the Deep Customer Insight Innovation Framework in order to develop an understanding as to how design can be integrated within existing innovation processes. The Deep Customer Insight Innovation Framework synthesises the work of Beckman and Barry (2007) as a theoretical foundation, with the framework explored within a case study of Australian Airport Corporation seeking to drive airport innovations in operations and retail performance. The integration of a deep customer insight approach develops customer-centric and highly integrated solutions as a function of concentrated problem exploration and design-led idea generation. Businesses’ facing complex innovation challenges or seeking to making sense of future opportunities will be able to integrate design into existing innovation processes, anchoring the new approach between existing market research and business development activities. This paper contributes a framework and novel understanding as to how design methods are integrated into existing innovation processes for operationalization within industry.

Substrate, molecular structure, and solvent effects in 2D self-assembly via hydrogen and halogen bonding

Recently, halogen···halogen interactions have been demonstrated to stabilize two-dimensional supramolecular assemblies at the liquid–solid interface. Here we study the effect of changing the halogen, and report on the 2D supramolecular structures obtained by the adsorption of 2,4,6-tris(4-bromophenyl)-1,3,5-triazine (TBPT) and 2,4,6-tris(4-iodophenyl)-1,3,5-triazine (TIPT) on both highly oriented pyrolytic graphite and the (111) facet of a gold single crystal. These molecular systems were investigated by combining room-temperature scanning tunneling microscopy in ambient conditions with density functional theory, and are compared to results reported in the literature for the similar molecules 1,3,5-tri(4-bromophenyl)benzene (TBPB) and 1,3,5-tri(4-iodophenyl)benzene (TIPB). We find that the substrate exerts a much stronger effect than the nature of the halogen atoms in the molecular building blocks. Our results indicate that the triazine core, which renders TBPT and TIPT stiff and planar, leads to stronger adsorption energies and hence structures that are different from those found for TBPB and TIPB. On the reconstructed Au(111) surface we find that the TBPT network is sensitive to the fcc- and hcp-stacked regions, indicating a significant substrate effect. This makes TBPT the first molecule reported to form a continuous monolayer at room temperature in which molecular packing is altered on the differently reconstructed regions of the Au(111) surface. Solvent-dependent polymorphs with solvent coadsorption were observed for TBPT on HOPG. This is the first example of a multicomponent self-assembled molecular networks involving the rare cyclic, hydrogen-bonded hexamer of carboxylic groups, R66(24) synthon.

Molecular theory for the effects of specific solute-solvent interaction on the diffusion of a solute particle in a molecular liquid

An understanding of the effect of specific solute-solvent interactions on the diffusion of a solute probe is a long standing problem of physical chemistry. In this paper a microscopic treatment of this effect is presented. The theory takes into account the modification of the solvent structure around the solute due to this specific interaction between them. It is found that for strong, attractive interaction, there is an enhanced coupling between the solute and the solvent dynamic modes (in particular, the density mode), which leads to a significant increase in the friction on the solute. The diffusion coefficient of the solute is found to depend strongly and nonlinearly on the magnitude of the attractive interaction. An interesting observation is that specific solute-solvent interaction can induce a crossover from a sliplike to a sticklike diffusion. In the limit of strong attractive interaction, we recover a dynamic version of the solvent-berg picture. On the other hand, for repulsive interaction, the diffusion coefficient of the solute increases. These results are in qualitative agreement with recent experimental observations.

Identification of the nature of platinum related midgap state in silicon by deep level transient spectroscopy

In this article, we present the detailed investigations on platinum related midgap state corresponding to E-c -0.52 eV probed by deep level transient spectroscopy. By irradiating the platinum doped samples with high-energy (1.1 MeV) gamma rays, we observed that the concentration of the midgap state increases and follows a square dependence with irradiation dose. However, the concentration of the acceptor corresponding to E-c -20.28 eV remained constant. Furthermore, from the studies on passivation by atomic hydrogen and thermal reactivation, we noticed that the E-c -0.52 eV level reappears in the samples annealed at high temperatures after hydrogenation. The interaction of platinum with various defects and the qualitative arguments based on the law of mass action suggest that the platinum related midgap defect might possibly correspond to the interstitial platinum-divacancy complex (V-Pt-V).

The effect of ball milling on the melting behavior of Sn-Cu-Ag eutectic alloy

Sn-Ag-Cu (SAC) solder alloys are the best Pb free alternative for electronic industry. Since their introduction, efforts are made to improve their efficacies by tuning the processing and composition to achieve lower melting point and better wettability. Nanostructured alloys with large boundary content are known to depress the melting points of metals and alloys. In this article we explore this possibility by processing prealloyed SAC alloys close to SAC305 composition (Sn-3wt%Ag-0.5wt%Cu) by mechanical milling which results in the formation of nanostructured alloys. Pulverisette ball mill (P7) and Vibratory ball mills are used to carry out the milling of the powders at room temperature and at lower temperatures (-104 A degrees C), respectively. We report a relatively smaller depression of melting point ranging up to 5 A degrees C with respect to original alloys. The minimum grain sizes achieved and the depression of melting point are similar for both room temperature and low-temperature processed samples. An attempt has been made to rationalize the observations in terms of the basic processes occurring during the milling.