Description:
Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.
Description:
Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms.
Description:
Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms.
Description:
In uncertain contemporary times, Initial Teacher Education (ITE) educators are under considerable pressure from political, social and institutional sources to ensure that PSTs are “classroom ready”; fully equipped to prepare diverse student cohorts to lead fulfilling post-school lives in an increasingly complex and changing world. To achieve this goal, current research and policy is increasingly focusing on foregrounding inclusive teaching practice. A great deal of education literature focuses on notions of diversity and inclusion with regard to student education in schools (Blackmore, 2009, Campbell & Whitty, 2002, Nieto, 1999, Smyth & McInerney, 2007, and Smyth & McInerney, 2009). Much has also been written about the difficulties inherent in educating PSTs regarding the complexities of inclusive teaching (Blackmore, 2009, Shor, 1992, Sleeter, 2001, Smyth & McInerney, 2007, and 2018 ATEA & TEFANZ Conference: Teacher Education in and for Uncertain Times Smyth & McInerney, 2009). In addition, leading education organizations and accrediting institutions, such as the Victorian Institute of Teaching (VIT) and the Australian Institute for Teaching and School Leadership (AITSL) promote inclusion as a mandated teaching approach. While inclusion is the approach mandated in Australia for catering for diversity, the authors argue that current notions of inclusive teaching are still haunted by ghosts of integration and other non-inclusive practices in approaches that hierarchical, additive and focused on deficit thinking. In this model, students are diagnosed as having a particular condition, disorder, impairment, or other difference, which is prioritized as their chief learning characteristic. The rich complexity of a learner’s strengths, preferences, challenges and goals is then narrowed down to one major ingredient – the impairment or difference – which becomes the focus of strategies and practices recommended as appropriate for that particular condition. In this presentation, the authors ask the thorny question, how do we teach PSTs to identify the complexity of learner needs and to make pedagogic decisions to enable learning to occur for all students? The presentation contributes a way forward through the authors’ examination of a range of pedagogies used in class to facilitate PSTs’ investigation of approaches and practices that encourage teaching for inclusion. In order to facilitate PSTs’ learning concerning inclusive teaching, the authors focus on creating opportunities to enable students to work with a diverse range of learners “selecting strategies on the basis of what is to be learnt rather than what is wrong with the learner” (Florian, 2008, p. 2004). As an exploration of pedagogic decisions and teaching approaches, the paper outlines a case study that makes use of a self-study methodology as well as discourse analysis. This research mode “includes elements of ongoing inquiry, respects personal experience, and emphasizes the role of knowledge construction. The collaborative component of self-study acknowledges the important role of the social construction of knowledge (Lassonde, Galman & Kosnik, 2009, p. 10). The inclusivity of a self-study approach and its multi-faceted nature encourages reflection, collaboration and on-going dialogue between educators and PSTs providing insights into teaching practices. From observations and reflective examination of their teaching practices and course development, the authors identify and analyse the pedagogies that are being used to achieve the aims of promoting teaching for inclusion in ITE courses. In addition to a self-study methodology, discourse analysis is used to examine formal literature and policy discussing diversity and inclusion."
Description:
Manufacturing industries today are focusing on controlling negative environmental impacts, reducing cost, conserving energy and natural resources while producing new products through various methods and tools of Sustainable manufacturing assessment. This paper proposes science-based guidelines for modeling and evaluation of key performance indicators (KPIs) in an aluminium extrusion process. The proposed methodology consists of the following main components: 1) identification of key performance indicators 2) classification of manufacturing processes on the basis of KPIs 3) evaluation of key performance indicators. We discuss in detail the steps of the proposed indicators for aluminium extrusion process. The proposed indicators also contribute towards the development of standard reference methodology to help further sustainability in the manufacturing industries. In this paper we propose science-based guidelines, which can be used to determine key performance indicators, namely energy consumption, solid waste and carbon emissions of an aluminium extrusion process. The proposed methodology has been verified with actual shop floor data obtained from the industry.
Description:
Most density-based clustering algorithms suffer from large density variations among clusters. This paper proposes a new measure called Neighbourhood Contrast (NC) as a better alternative to density in detecting clusters. The proposed NC admits all local density maxima, regardless of their densities, to have similar NC values. Due to this unique property, NC is a better means to detect clusters in a dataset with large density variations among clusters. We provide two applications of NC. First, replacing density with NC in the current state-of-the-art clustering procedure DP leads to significantly improved clustering performance. Second, we devise a new clustering algorithm called Neighbourhood Contrast Clustering (NCC) which does not require density or distance calculations, and therefore has a linear time complexity in terms of dataset size. Our empirical evaluation shows that both NC-based methods outperform density-based methods including the current state-of-the-art.
Description:
Background: This regional pilot site ‘end-user attitudes’ study explored nurses’ experiences and impressions of using the Nurses’ Behavioural Assistant (NBA) (a knowledge-based, interactive ehealth system) to assist them to better respond to behavioural and psychological symptoms of dementia (BPSD) and will be reported here. Methods: Focus groups were conducted, followed by a four-week pilot site ‘end-user attitudes’ trial of the NBA at a regional aged care residential facility (ACRF). Brief interviews were conducted with consenting nursing staff. Results: Focus group feedback (N = 10) required only minor cosmetic changes to the NBA prototype. Post pilot site end-user interview data (N = 10) indicated that the regional ACRF nurses were positive and enthusiastic about the NBA, however several issues were also identified. Conclusions: Overall the results supported the utility of the NBA to promote a person centred care approach to managing BPSD. Slight modifications may be required to maximise its uptake across all ACRF nursing staff.
Description:
Desiccation cracking is a common, undesirable phenomenon in many geotechnical engineering applications, particularly in compacted clay liners as it causes significant changes to the hydraulic and mechanical properties of soils. This is one of the major concerns in design and construction of landfill clay liners in arid regions. This paper reports the findings of laboratory tests conducted to investigate the effects of adding nylon fibre, guar gum and organic enzyme to the soil to control desiccation cracks. Merri-Creek clay, a highly expansive soil from Victoria, Australia, was used in the tests. The additives were mixed with soil in different combinations and in varying proportions. Desiccation tests were conducted in thin, long moulds as in the free shrinkage tests. It was observed that very small quantities of these additives were sufficient to alter the properties of overall mixture. The results indicated that fibre – enzyme is the most effective combination to reduce desiccation cracks. This additive combination contained 0.3% percent of fibres by dry weight of soil and 0.35 g of enzyme per kg of dry soil. There was a modest improvement in dry density in the fibre -enzyme mixture compared to pure Merri-Creek clay. Fibre-enzyme combination also showed the ability to reduce the hydraulic conductivity. It was discovered that guar gum is not desirable to mix with clays in terms of reducing desiccation cracking or decreasing hydraulic conductivity.
Description:
Multi-instance multi-label learning (MIML) is a learning paradigm where an object is represented by a bag of instances and each bag is associated with multiple labels. Ordinary MIML setting assumes a fixed target label set. In real applications, multiple novel labels may exist outside this set, but hidden in the training data and unknown to the MIML learner. Existing MIML approaches are unable to discover the hidden novel labels, let alone predicting these labels in the previously unseen test data. In this paper, we propose the first approach to discover multiple novel labels in MIML problem using an efficient augmented lagrangian optimization, which has a bag-dependent loss term and a bag-independent clustering regularization term, enabling the known labels and multiple novel labels to be modeled simultaneously. The effectiveness of the proposed approach is validated in experiments.
Description:
The protocol diversities of mobile ad hoc have already got hold of the field to a peak of a matured and developed area. Still, the restraint of delay and bandwidth of mobile ad hoc network have kept a little room to draft a routing protocol for the pursuit of providing quality of service. In the paper, we proposed protocol namely Efficient Route Selection in Ad Hoc On-Demand Distance Vector Routing. We select the best path among multiple paths from source to destination using covariance and delay. We consider the delay, link stability and energy to devise a covariance-based metric to discover the most balanced path. We also propose a metric for the selection of a node that acts as a local backup node for the most vulnerable nodes on the selected path. We accomplish our implementation in NS3and it shows the more reliable path and less end to end delay than other counterpart protocols.
Description:
The protocol diversities of mobile ad hoc have already got hold of the field to a peak of a matured and developed area. Still, the restraint of delay and bandwidth of mobile ad hoc network have kept a little room to draft a routing protocol for the pursuit of providing quality of service. In the paper, we proposed protocol namely Efficient Route Selection in Ad Hoc On-Demand Distance Vector Routing. We select the best path among multiple paths from source to destination using covariance and delay. We consider the delay, link stability and energy to devise a covariance-based metric to discover the most balanced path. We also propose a metric for the selection of a node that acts as a local backup node for the most vulnerable nodes on the selected path. We accomplish our implementation in NS3and it shows the more reliable path and less end to end delay than other counterpart protocols.