Target reliability based design optimization of anchored cantilever sheet pile walls

In this study, the stability of anchored cantilever sheet pile wall in sandy soils is investigated using reliability analysis. Targeted stability is formulated as an optimization problem in the framework of an inverse first order reliability method. A sensitivity analysis is conducted to investigate the effect of parameters influencing the stability of sheet pile wall. Backfill soil properties, soil - steel pile interface friction angle, depth of the water table from the top of the sheet pile wall, total depth of embedment below the dredge line, yield strength of steel, section modulus of steel sheet pile, and anchor pull are all treated as random variables. The sheet pile wall system is modeled as a series of failure mode combination. Penetration depth, anchor pull, and section modulus are calculated for various target component and system reliability indices based on three limit states. These are: rotational failure about the position of the anchor rod, expressed in terms of moment ratio; sliding failure mode, expressed in terms of force ratio; and flexural failure of the steel sheet pile wall, expressed in terms of the section modulus ratio. An attempt is made to propose reliability based design charts considering the failure criteria as well as the variability in the parameters. The results of the study are compared with studies in the literature.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Epigenetics Underpinning DNA Damage Repair

Epigenetics is the study of heritable changes in gene expression that are not the result of genetic alterations. These changes include DNA methylation, histone modifications, or indeed microRNA expression. Chromatin is a tightly compacted DNA–protein complex that allows approximately two meters of DNA to be packaged inside a cell, only a few micrometers across. Although the resulting DNA structure is very stable, it is not very amiable to DNA-dependent processes, so mechanisms have to exist to allow processes such as transcription, replication, and DNA repair to occur. This chapter will look at how a cell responds to and deals with genomic instability at the epigenetic level and highlight how critical chromatin remodeling is for correct DNA repair and cell survival following DNA damage. This chapter will initially look at the DNA repair pathways that function in human cells and then at how the repair of DNA damage is controlled by epigenetics.

KAT5 (Tip60) is a potential therapeutic target in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is a rare aggressive cancer of the pleura. Asbestos exposure (through inhalation) is the most well established risk factor for mesothelioma. The current standard of care for patients suffering from MPM is a combination of cisplatin and pemetrexed (or alternatively cisplatin and raltitrexed). Most patients, however, die within 24 months of diagnosis. New therapies are therefore urgently required for this disease. Lysine acetyltransferases (KATs) including KAT5 have been linked with the development of cisplatin resistance. This gene may therefore be altered in MPM and could represent a novel candidate target for intervention. Using RT-PCR screening the expression of all known KAT5 variants was found to be markedly increased in malignant tumors compared to benign pleura. When separated according to histological subtype, KAT5 was significantly overexpressed in both the sarcomatoid and biphasic subgroups for all transcript variants. A panel of MPM cell lines including the normal pleural cells LP9 and Met5A was screened for expression of KAT5 variants. Treatment of cells with a small molecule inhibitor of KAT5 (MG-149) caused significant inhibition of cellular proliferation (p<0.0001), induction of apoptosis and was accompanied by significant induction of pro-inflammatory cytokines/chemokines.

Vibration characteristics and damage detection in a suspension bridge

Suspension bridges are flexible and vibration sensitive structures that exhibit complex and multi-modal vibration. Due to this, the usual vibration based methods could face a challenge when used for damage detection in these structures. This paper develops and applies a mode shape component specific damage index (DI) to detect and locate damage in a suspension bridge with pre-tensioned cables. This is important as suspension bridges are large structures and damage in them during their long service lives could easily go un-noticed. The capability of the proposed vibration based DI is demonstrated through its application to detect and locate single and multiple damages with varied locations and severity in the cables of the suspension bridge. The outcome of this research will enhance the safety and performance of these bridges which play an important role in the transport network.

Modelling Cued-Target Recall using Quantum Inspired Models of Target Activation

This article presents and evaluates Quantum Inspired models of Target Activation using Cued-Target Recall Memory Modelling over multiple sources of Free Association data. Two components were evaluated: Whether Quantum Inspired models of Target Activation would provide a better framework than their classical psychological counterparts and how robust these models are across the different sources of Free Association data. In previous work, a formal model of cued-target recall did not exist and as such Target Activation was unable to be assessed directly. Further to that, the data source used was suspected of suffering from temporal and geographical bias. As a consequence, Target Activation was measured against cued-target recall data as an approximation of performance. Since then, a formal model of cued-target recall (PIER3) has been developed [10] with alternative sources of data also becoming available. This allowed us to directly model target activation in cued-target recall with human cued-target recall pairs and use multiply sources of Free Association Data. Featural Characteristics known to be important to Target Activation were measured for each of the data sources to identify any major differences that may explain variations in performance for each of the models. Each of the activation models were used in the PIER3 memory model for each of the data sources and was benchmarked against cued-target recall pairs provided by the University of South Florida (USF). Two methods where used to evaluate performance. The first involved measuring the divergence between the sets of results using the Kullback Leibler (KL) divergence with the second utilizing a previous statistical analysis of the errors [9]. Of the three sources of data, two were sourced from human subjects being the USF Free Association Norms and the University of Leuven (UL) Free Association Networks. The third was sourced from a new method put forward by Galea and Bruza, 2015 in which pseudo Free Association Networks (Corpus Based Association Networks - CANs) are built using co-occurrence statistics on large text corpus. It was found that the Quantum Inspired Models of Target Activation not only outperformed the classical psychological model but was more robust across a variety of data sources.

Decomposition analysis to examine Australia’s 2030 GHGs emissions target: How hard will it be to achieve?

The Australian government has recently pledged a reduction in GHGs emissions of 26–28% below the 2005 level by 2030. How big is the challenge for the country to achieve this target in terms of its present emissions profile, recent historical trends, and the contributions to those trends from key proximate factors contributing to emissions? In this paper, we attempt a quantitative judgement of the challenge by using decomposition analysis. Based on the analysis it appears the announced target will be quite challenging to achieve if the average annual mitigating effects from economic restructuring, energy efficiency improvements and movement towards less emissions-intensive energy sources in evidence over 2002–2013 continued through to 2030; however, if the contribution from these mitigating sources in evidence over 2006–2013 can be sustained, achievement of the target will be much less challenging. The challenge for government then will be to provide a policy framework to ensure the more pronounced beneficial impacts of the mitigating factors evidenced during 2006–2013 can be maintained over the years to 2030.

Proteomic identification of putative microRNA394 target genes in Arabidopsis thaliana identifies MLP family members critical for normal development

Expression of the F-Box protein Leaf Curling Responsiveness (LCR) is regulated by microRNA, miR394, and alterations to this interplay in Arabidopsis thaliana produce defects in leaf polarity and shoot apical meristem (SAM) organisation. Although the miR394-LCR node has been documented in Arabidopsis, the identification of proteins targeted by LCR F-box itself has proven problematic. Here, a proteomic analysis of shoot apices from plants with altered LCR levels identified a member of the Major Latex Protein (MLP) family gene as a potential LCR F-box target. Bioinformatic and molecular analyses also suggested that other MLP family members are likely to be targets for this post-translational regulation. Direct interaction between LCR F-Box and MLP423 was validated. Additional MLP members had reduction in protein accumulation, in varying degrees, mediated by LCR F-Box. Transgenic Arabidopsis lines, in which MLP28 expression was reduced through an artificial miRNA technology, displayed severe developmental defects, including changes in leaf patterning and morphology, shoot apex defects, and eventual premature death. These phenotypic characteristics resemble those of Arabidopsis plants modified to over-express LCR. Taken together, the results demonstrate that MLPs are driven to degradation by LCR, and indicate that MLP gene family is target of miR394-LCR regulatory node, representing potential targets for directly post-translational regulation mediated by LCR F-Box. In addition, MLP28 family member is associated with the LCR regulation that is critical for normal Arabidopsis development.

Case management in action: An examination of two cases in the area of alcohol related brain damage

‘Practice Forum’ provides a forum for social work practitioners to share their practice with others; to describe what they are doing and assess its effectiveness. The practice of case management is applied in a wide range of service delivery models to meet complex client needs. Unfortunately, cost containment and lack of clarity of the role of the case manager has blurred the definition and practice of case management for both the consumer and professional providers. This article examines two cases of a small non-government agency in Melbourne called Alcohol Related Brain Injury Assessment, Accommodation & Support Inc. (ARBIAS) where case management services are delivered to people with alcohol acquired brain damage. The analysis presented here supports the view that continuity of care and intensive relationship building with clients is vital for successful client outcomes and has application to a variety of programs which service chronically disabled clients.

Parametric analysis of acoustic emission signals for evaluating damage in composites using a PVDF film sensor

With the increased utilization of advanced composites in strategic industries, the concept of Structural Health Monitoring (SHM) with its inherent advantages is gaining ground over the conventional methods of NDE and NDI. The most attractive feature of this concept is on-line evaluation using embedded sensors. Consequently, development of methodologies with identification of appropriate sensors such as PVDF films becomes the key for exploiting the new concept. And, of the methods used for on-line evaluation acoustic emission has been most effective. Thus, Acoustic Emission (AE) generated during static tensile loading of glass fiber reinforced plastic composites was monitored using a Polyvinylidene fluoride (PVDF) film sensor. The frequency response of the film sensor was obtained with pencil lead breakage tests to choose the appropriate band of operation. The specimen considered for the experiments were chosen to characterize the differences in the operation of the failure mechanisms through AE parametric analysis. The results of the investigations can be characterized using AE parameter indicating that a PVDF film sensor was effective as an AE sensor used in structural health monitoring on-line.