A ‘Value Ecology’ approach to the performing arts

In recent years, ecological thinking has been applied to a range of social, cultural and aesthetic systems, including performing arts as a living system of policy makers, producers, organisations, artists and audiences. Ecological thinking is systems-based thinking which allows us to see the performing arts as a complex and protean ecosystem; to explain how elements in this system act and interact; and to evaluate its effects on Australia’s social fabric over time. According to Gallasch, ecological thinking is “what we desperately need for the arts.” It enables us to “defeat the fragmentary and utilitarian view of the arts that dominates, to make connections, to establish overviews of the arts that can be shared and debated” (Gallasch NP). John Baylis took up these issues in "Mapping Queensland Theatre" (2009), an Arts Queensland-funded survey designed to map practices in Brisbane and in Queensland more broadly, and to provide a platform to support future policy-making. In this paper, we propose a new approach to mapping Brisbane’s and Queensland’s theatre that extends Baylis’ ‘value chain’ into a ‘value ecology’ that provides a more textured picture of players, patterns, relationships and activity levels in local performing arts.

A simulated annealing algorithm for energy efficient virtual machine placement

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.

Self-assembled hyperbranched polymer - gold nanoparticle hybrids : understanding the effect of polymer coverage on assembly size and SERS performance

In the past few years, remarkable progress has been made in unveiling novel and unique optical properties of strongly coupled plasmonic nanostructures. However, application of such plasmonic nanostructures in biomedicine remains challenging due to the lack of facile and robust assembly methods for producing stable nanostructures. Previous attempts to achieve plasmonic nano-assemblies using molecular ligands were limited due to the lack of flexibility that could be exercised in forming them. Here, we report the utilization of tailor-made hyperbranched polymers (HBP) as linkers to assemble gold nanoparticles (NPs) into nano-assemblies. The ease and flexibility in tuning the particle size and number of branch ends of a HBP makes it an ideal candidate as a linker, as opposed to DNA, small organic molecules and linear or dendrimeric polymers. We report a strong correlation of polymer (HBP) concentration with the size of the hybrid nano-assemblies and “hot-spot” density. We have shown that such solutions of stable HBP-gold nano-assemblies can be barcoded with various Raman tags to provide improved surface-enhanced Raman scattering (SERS) compared with non-aggregated NP systems. These Raman barcoded hybrid nano-assemblies, with further optimization of NP shape, size and “hot-spot” density, may find application as diagnostic tools in nanomedicine.

Selective reductions using visible light photocatalysts of supported gold nanoparticles

Photocatalytic synthesis using visible light is a desirable chemical process because of its potential to utilize sunlight. Supported gold nanoparticles (Au-NPs) were found to be efficient photocatalysts and the effects of the supports were identified including CeO2, TiO2, ZrO2, Al2O3, and zeolite Y. In particular Au/CeO2 exhibited the high catalytic activity to reduce nitroaromatics to azo compounds, hydrogenate azobenzene to hydroazobenzene, reduce ketones to alcohols, and deoxygenate epoxides to alkenes at ambient temperatures, under irradiation of visible light (or simulated sunlight). The reac-tive efficiency depends on two primary factors: one is the light adsorption of catalysts and another is the driving ability of catalysts corresponding to the reactants. The light absorption by Au-NPs is due to surface plasmon resonance effect or inter-band electron transition; this is related to the reduction ability of the photocatalysts. Irradiation with shorter wavelengths can excite the conduction electrons in Au-NPs to higher energy levels and as a result, induce reduction with more negative reduction potentials. It is known when irradiated with light the Au-NPs can abstract hydrogen from isopropanol forming Au-H species on the Au-NP surface. Hence, we proposed that the active Au-H species will react with the N=O, N=N, C=O double bonds or epoxide bonds, which are weakened by the interaction with the excited electrons in the Au-NPs, and yield the final reductive products. The reacting power of the Au-H species depends on the energy of the excited electrons in Au-NPs: the higher the electronic energy, the stronger the reduction ability of the Au-H species. This finding demonstrates that we can tune the reduction ability of the photocatalysts by manipulating the irradiation wavelength.

Structural dissection of Ebola virus and its assembly determinants using cryo-electron tomography

Ebola virus is a highly pathogenic filovirus causing severe hemorrhagic fever with high mortality rates. It assembles heterogenous, filamentous, enveloped virus particles containing a negative-sense, single-stranded RNA genome packaged within a helical nucleocapsid (NC). We have used cryo-electron microscopy and tomography to visualize Ebola virus particles, as well as Ebola virus-like particles, in three dimensions in a near-native state. The NC within the virion forms a left-handed helix with an inner nucleoprotein layer decorated with protruding arms composed of VP24 and VP35. A comparison with the closely related Marburg virus shows that the N-terminal region of nucleoprotein defines the inner diameter of the Ebola virus NC, whereas the RNA genome defines its length. Binding of the nucleoprotein to RNA can assemble a loosely coiled NC-like structure; the loose coil can be condensed by binding of the viral matrix protein VP40 to the C terminus of the nucleoprotein, and rigidified by binding of VP24 and VP35 to alternate copies of the nucleoprotein. Four proteins (NP, VP24, VP35, and VP40) are necessary and sufficient to mediate assembly of an NC with structure, symmetry, variability, and flexibility indistinguishable from that in Ebola virus particles released from infected cells. Together these data provide a structural and architectural description of Ebola virus and define the roles of viral proteins in its structure and assembly

Optimally increasing secure connectivity in multihop wireless ad hoc networks

We consider the problem of how to maximize secure connectivity of multi-hop wireless ad hoc networks after deployment. Two approaches, based on graph augmentation problems with nonlinear edge costs, are formulated. The first one is based on establishing a secret key using only the links that are already secured by secret keys. This problem is in NP-hard and does not accept polynomial time approximation scheme PTAS since minimum cutsets to be augmented do not admit constant costs. The second one is based of increasing the power level between a pair of nodes that has a secret key to enable them physically connect. This problem can be formulated as the optimal key establishment problem with interference constraints with bi-objectives: (i) maximizing the concurrent key establishment flow, (ii) minimizing the cost. We show that both problems are NP-hard and MAX-SNP (i.e., it is NP-hard to approximate them within a factor of 1 + e for e > 0 ) with a reduction to MAX3SAT problem. Thus, we design and implement a fully distributed algorithm for authenticated key establishment in wireless sensor networks where each sensor knows only its one- hop neighborhood. Our witness based approaches find witnesses in multi-hop neighborhood to authenticate the key establishment between two sensor nodes which do not share a key and which are not connected through a secure path.

Design and analysis of key management schemes for distributed wireless sensor networks

Secure communications in distributed Wireless Sensor Networks (WSN) operating under adversarial conditions necessitate efficient key management schemes. In the absence of a priori knowledge of post-deployment network configuration and due to limited resources at sensor nodes, key management schemes cannot be based on post-deployment computations. Instead, a list of keys, called a key-chain, is distributed to each sensor node before the deployment. For secure communication, either two nodes should have a key in common in their key-chains, or they should establish a key through a secure-path on which every link is secured with a key. We first provide a comparative survey of well known key management solutions for WSN. Probabilistic, deterministic and hybrid key management solutions are presented, and they are compared based on their security properties and re-source usage. We provide a taxonomy of solutions, and identify trade-offs in them to conclude that there is no one size-fits-all solution. Second, we design and analyze deterministic and hybrid techniques to distribute pair-wise keys to sensor nodes before the deployment. We present novel deterministic and hybrid approaches based on combinatorial design theory and graph theory for deciding how many and which keys to assign to each key-chain before the sensor network deployment. Performance and security of the proposed schemes are studied both analytically and computationally. Third, we address the key establishment problem in WSN which requires key agreement algorithms without authentication are executed over a secure-path. The length of the secure-path impacts the power consumption and the initialization delay for a WSN before it becomes operational. We formulate the key establishment problem as a constrained bi-objective optimization problem, break it into two sub-problems, and show that they are both NP-Hard and MAX-SNP-Hard. Having established inapproximability results, we focus on addressing the authentication problem that prevents key agreement algorithms to be used directly over a wireless link. We present a fully distributed algorithm where each pair of nodes can establish a key with authentication by using their neighbors as the witnesses.

Who's asking for what? Fundraising and leadership in Australian nonprofits

The nonprofit funding landscape is in flux. Many organisations are having to think differently and develop fresh skills either to enter the fundraising market or to cope better with rising competition for community and corporate support. This new reality affects boards, CEOs and fundraisers alike. Against this backdrop, our exploratory study aimed to build an evidence base and spark more discussion about: - the role Australian nonprofit CEOs and boards play in supporting fundraising/development; - current engagement levels; and - perceptions of leadership in fundraising from two possibly contrasting perspectives: NP leaders (board members and CEOs); and fundraisers. This research has been supported by the Perpetual Foundation, the EF and SL Gluyas Trust and the Edward Corbould Charitable Trust under the management of Perpetual Trustee Company Ltd.

Complexity of increasing the secure connectivity in wireless ad hoc networks

We consider the problem of maximizing the secure connectivity in wireless ad hoc networks, and analyze complexity of the post-deployment key establishment process constrained by physical layer properties such as connectivity, energy consumption and interference. Two approaches, based on graph augmentation problems with nonlinear edge costs, are formulated. The first one is based on establishing a secret key using only the links that are already secured by shared keys. This problem is in NP-hard and does not accept polynomial time approximation scheme PTAS since minimum cutsets to be augmented do not admit constant costs. The second one extends the first problem by increasing the power level between a pair of nodes that has a secret key to enable them physically connect. This problem can be formulated as the optimal key establishment problem with interference constraints with bi-objectives: (i) maximizing the concurrent key establishment flow, (ii) minimizing the cost. We prove that both problems are NP-hard and MAX-SNP with a reduction to MAX3SAT problem.

SERS based detection of barcoded gold nanoparticle assemblies from within animal tissue

We have explored the potential of deep Raman spectroscopy, specifically surface enhanced spatially offset Raman spectroscopy (SESORS), for non-invasive detection from within animal tissue, by employing SERS-barcoded nanoparticle (NP) assemblies as the diagnostic agent. This concept has been experimentally verified in a clinic-relevant backscattered Raman system with an excitation line of 785 nm under ex vivo conditions. We have shown that our SORS system, with a fixed offset of 2-3 mm, offered sensitive probing of injected QTH-barcoded NP assemblies through animal tissue containing both protein and lipid. In comparison to that of non-aggregated SERS-barcoded gold NPs, we have demonstrated that the tailored SERS-barcoded aggregated NP assemblies have significantly higher detection sensitivity. We report that these NP assemblies can be readily detected at depths of 7-8 mm from within animal proteinaceous tissue with high signal-to-noise (S/N) ratio. In addition they could also be detected from beneath 1-2 mm of animal tissue with high lipid content, which generally poses a challenge due to high absorption of lipids in the near-infrared region. We have also shown that the signal intensity and S/N ratio at a particular depth is a function of the SERS tag concentration used and that our SORS system has a QTH detection limit of 10-6 M. Higher detection depths may possibly be obtained with optimization of the NP assemblies, along with improvements in the instrumentation. Such NP assemblies offer prospects for in vivo, non-invasive detection of tumours along with scope for incorporation of drugs and their targeted and controlled release at tumour sites. These diagnostic agents combined with drug delivery systems could serve as a “theranostic agent”, an integration of diagnostics and therapeutics into a single platform.

A novel method for finding similarities between unordered trees using matrix data model

Trees are capable of portraying the semi-structured data which is common in web domain. Finding similarities between trees is mandatory for several applications that deal with semi-structured data. Existing similarity methods examine a pair of trees by comparing through nodes and paths of two trees, and find the similarity between them. However, these methods provide unfavorable results for unordered tree data and result in yielding NP-hard or MAX-SNP hard complexity. In this paper, we present a novel method that encodes a tree with an optimal traversing approach first, and then, utilizes it to model the tree with its equivalent matrix representation for finding similarity between unordered trees efficiently. Empirical analysis shows that the proposed method is able to achieve high accuracy even on the large data sets.

Phospho-Akt expression is associated with a favorable outcome in non-small cell lung cancer

Akt, a Serine/Threonine protein kinase, mediates growth factor-associated cell survival. Constitutive activation of Akt (phosphorylated Akt, P-Akt) has been observed in several human cancers, including lung cancer and may be associated with poor prognosis and chemotherapy and radiotherapy resistance. The clinical relevance of P-Akt in non-small cell lung cancer (NSCLC) is not well described. In the present study, we examined 82 surgically resected snap-frozen and paraffin-embedded stage I to IIIA NSCLC samples for P-Akt and Akt by Western blotting and for P-Akt by immunohistochemistry. P-Akt protein levels above the median, measured using reproducible semiquantitative band densitometry, correlated with a favorable outcome (P = 0.007). Multivariate analysis identified P-Akt as a significant independent favorable prognostic factor (P = 0.004). Although associated with a favorable prognosis, high P-Akt levels correlated with high tumor grade (P = 0.02). Adenocarcinomas were associated with low P-Akt levels (P = 0.039). Akt was not associated with either outcome or clinicopathologic variables. Cytoplasmic (CP-Akt) and nuclear (NP-Akt) P-Akt tumor cell staining was detected in 96% and 42% of cases, respectively. Both CP-Akt and NP-Akt correlated with well-differentiated tumors (P = 0.008 and 0.017, respectively). NP-Akt also correlated with nodal metastases (P = 0.022) and squamous histology (P = 0.037). These results suggest P-Akt expression is a favorable prognostic factor in NSCLC. Immunolocalization of P-Akt, however, may be relevant as NP-Akt was associated with nodal metastases, a known poor prognostic feature in this disease. P-Akt may be a potential novel therapeutic target for the management of NSCLC. © 2005 American Association for Cancer Research.

Effect of chemical conjugation of L-leucine to chitosan on the dispersibility and drug release of a dry powder inhaler nanoparticle formulation

Purpose: This study investigated the effect of chemical conjugation of the amino acid L-leucine to the polysaccharide chitosan on the dispersibility and drug release pattern of a polymeric nanoparticle (NP)-based controlled release dry powder inhaler (DPI) formulation. Methods: A chemical conjugate of L-leucine with chitosan was synthesized and characterized by Infrared (IR) Spectroscopy, Nuclear Magnetic Resonance (NMR) Spectroscopy, Elemental Analysis and X-ray Photoelectron Spectroscopy (XPS). Nanoparticles of both chitosan and its conjugate were prepared by a water-in-oil emulsification – glutaraldehyde cross-linking method using the antihypertensive agent, diltiazem (Dz) hydrochloride as the model drug. The surface morphology and particle size distribution of the nanoparticles were determined by Scanning Electron Microscopy (SEM) and Dynamic Light Scattering (DLS). The dispersibility of the nanoparticle formulation was analysed by a Twin Stage Impinger (TSI) with a Rotahaler as the DPI device. Deposition of the particles in the different stages was determined by gravimetry and the amount of drug released was analysed by UV spectrophotometry. The release profile of the drug was studied in phosphate buffered saline at 37 ⁰C and analyzed by UV spectrophotometry. Results: The TSI study revealed that the fine particle fractions (FPF), as determined gravimetrically, for empty and drug-loaded conjugate nanoparticles were significantly higher than for the corresponding chitosan nanoparticles (24±1.2% and 21±0.7% vs 19±1.2% and 15±1.5% respectively; n=3, p<0.05). The FPF of drug-loaded chitosan and conjugate nanoparticles, in terms of the amount of drug determined spectrophotometrically, had similar values (21±0.7% vs 16±1.6%). After an initial burst, both chitosan and conjugate nanoparticles showed controlled release that lasted about 8 to 10 days, but conjugate nanoparticles showed twice as much total drug release compared to chitosan nanoparticles (~50% vs ~25%). Conjugate nanoparticles also showed significantly higher dug loading and entrapment efficiency than chitosan nanoparticles (conjugate: 20±1% & 46±1%, chitosan: 16±1% & 38±1%, n=3, p<0.05). Conclusion: Although L-leucine conjugation to chitosan increased dispersibility of formulated nanoparticles, the FPF values are still far from optimum. The particles showed a high level of initial burst release (chitosan, 16% and conjugate, 31%) that also will need further optimization.

A hybrid constructive heuristic and simulated annealing for railway crew scheduling

Railway crew scheduling problem is the process of allocating train services to the crew duties based on the published train timetable while satisfying operational and contractual requirements. The problem is restricted by many constraints and it belongs to the class of NP-hard. In this paper, we develop a mathematical model for railway crew scheduling with the aim of minimising the number of crew duties by reducing idle transition times. Duties are generated by arranging scheduled trips over a set of duties and sequentially ordering the set of trips within each of duties. The optimisation model includes the time period of relief opportunities within which a train crew can be relieved at any relief point. Existing models and algorithms usually only consider relieving a crew at the beginning of the interval of relief opportunities which may be impractical. This model involves a large number of decision variables and constraints, and therefore a hybrid constructive heuristic with the simulated annealing search algorithm is applied to yield an optimal or near-optimal schedule. The performance of the proposed algorithms is evaluated by applying computational experiments on randomly generated test instances. The results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time for large-sized problems.

A comparative study of the semi-elastic and fully-elastic MapReduce models

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics and it can obtain a better solution in a reasonable time. Furthermore, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement which puts a fixed number of mapper/reducer on each machine. The comparison results show that the computation using our mapper/reducer placement is much cheaper than the computation using the conventional placement while still satisfying the computation deadline.