An empirical analysis of the factors and measures of Enterprise Architecture Management success

Enterprise Architecture Management (EAM) is discussed in academia and industry as a vehicle to guide IT implementations, alignment, compliance assessment, or technology management. Still, a lack of knowledge prevails about how EAM can be successfully used, and how positive impact can be realized from EAM. To determine these factors, we identify EAM success factors and measures through literature reviews and exploratory interviews and propose a theoretical model that explains key factors and measures of EAM success. We test our model with data collected from a cross-sectional survey of 133 EAM practitioners. The results confirm the existence of an impact of four distinct EAM success factors, ‘EAM product quality’, ‘EAM infrastructure quality’, ‘EAM service delivery quality’, and ‘EAM organizational anchoring’, and two important EAM success measures, ‘intentions to use EAM’ and ‘Organizational and Project Benefits’ in a confirmatory analysis of the model. We found the construct ‘EAM organizational anchoring’ to be a core focal concept that mediated the effect of success factors such as ‘EAM infrastructure quality’ and ‘EAM service quality’ on the success measures. We also found that ‘EAM satisfaction’ was irrelevant to determining or measuring success. We discuss implications for theory and EAM practice.

A Monte-Carlo tree search in argumentation

Monte-Carlo Tree Search (MCTS) is a heuristic to search in large trees. We apply it to argumentative puzzles where MCTS pursues the best argumentation with respect to a set of arguments to be argued. To make our ideas as widely applicable as possible, we integrate MCTS to an abstract setting for argumentation where the content of arguments is left unspecified. Experimental results show the pertinence of this integration for learning argumentations by comparing it with a basic reinforcement learning.

Optimisation of open pit mine block sequencing

This study presents a comprehensive mathematical model for open pit mine block sequencing problem which considers technical aspects of real-life mine operations. As the open pit block sequencing problem is an NP-hard, state-of-the-art heuristics algorithms, including constructive heuristic, local search, simulated annealing, and tabu search are developed and coded using MATLAB programming language. Computational experiments show that the proposed algorithms are satisfactory to solve industrial-scale instances. Numerical investigation and sensitivity analysis based on real-world data are also conducted to provide insightful and quantitative recommendations for mine schedulers and planners.

Separate or simultaneous removal of radioactive cations and anions from water by layered sodium vanadate-based sorbents

Nanofibers of sodium vanadate, consisting of very thin negatively charged layers and exchangeable sodium ions between the layers, are efficient sorbents for the removal of radioactive 137Cs+ and 85Sr2+ cations from water. The exchange of 137Cs+ or 85Sr2+ ions with the interlayer Na+ ions eventually triggered structural deformation of the thin layers, trapping the 137Cs+ and 85Sr2+ ions in the nanofibers. Furthermore, when the nanofibers were dispersed in a AgNO3 solution at pH >7, well-dispersed Ag2O nanocrystals formed by firmly anchoring themselves on the fiber surfaces along planes of crystallographic similarity with those of Ag2O. These nanocrystals can efficiently capture I– anions by forming a AgI precipitate, which was firmly attached to the substrates. We also designed sorbents that can remove 137Cs+ and 125I– ions simultaneously for safe disposal by optimizing the Ag2O loading and sodium content of the vanadate. This study confirms that sorbent features such as fibril morphology, negatively charged thin layers and readily exchangeable Na+ ions between the layers, and the crystal planes for the formation of a coherent interface with Ag2O nanocrystals on the fiber surface are very important for the simultaneous uptake of cations and anions.

SERS-barcoded colloidal gold NP assemblies as imaging agents for use in biodiagnostics

There is a growing need for new biodiagnostics that combine high throughput with enhanced spatial resolution and sensitivity. Gold nanoparticle (NP) assemblies with sub-10 nm particle spacing have the benefits of improving detection sensitivity via Surface enhanced Raman scattering (SERS) and being of potential use in biomedicine due to their colloidal stability. A promising and versatile approach to form solution-stable NP assemblies involves the use of multi-branched molecular linkers which allows tailoring of the assembly size, hot-spot density and interparticle distance. We have shown that linkers with multiple anchoring end-groups can be successfully employed as a linker to assemble gold NPs into dimers, linear NP chains and clustered NP assemblies. These NP assemblies with diameters of 30-120 nm are stable in solution and perform better as SERS substrates compared with single gold NPs, due to an increased hot-spot density. Thus, tailored gold NP assemblies are potential candidates for use as biomedical imaging agents. We observed that the hot-spot density and in-turn the SERS enhancement is a function of the linker polymer concentration and polymer architecture. New deep Raman techniques like Spatially Offset Raman Spectroscopy (SORS) have emerged that allow detection from beneath diffusely scattering opaque materials, including biological media such as animal tissue. We have been able to demonstrate that the gold NP assemblies could be detected from within both proteinaceous and high lipid containing animal tissue by employing a SORS technique with a backscattered geometry.

Genetic determinants of bone density and fracture risk: State of the art and future directions

Context: Osteoporosis is a common, highly heritable condition that causes substantial morbidity and mortality, the etiopathogenesis of which is poorly understood. Genetic studies are making increasingly rapid progress in identifying the genes involved. Evidence Acquisition and Synthesis: In this review, we will summarize the current understanding of the genetics of osteoporosis based on publications from PubMed from the year 1987 onward. Conclusions: Most genes involved in osteoporosis identified to date encode components of known pathways involved in bone synthesis or resorption, but as the field progresses, new pathways are being identified. Only a small proportion of the total genetic variation involved in osteoporosis has been identified, and new approaches will be required to identify most of the remaining genes.

Doing Digital Composition on the Social Web: Knowledge Processes in Literacy Learning

The power to influence others in ever-expanding social networks in the new knowledge economy is tied to capabilities with digital media production. This chapter draws on research in elementary classrooms to examine the repertoires of cross-disciplinary knowledge that literacy learners need to produce innovative digital media via the “social web”. It focuses on the knowledge processes that occurred when elementary students engaged in multimodal text production with new digital media. It draws on Kalantzis and Cope’s (2008) heuristic for theorizing “Knowledge Processes” in the Learning by Design approach to pedagogy. Learners demonstrate eight “Knowledge Processes” across different subject domains, skills areas, and sensibilities. Drawing data from media-based lessons across several classroom and schools, this chapter examines what kinds of knowledge students utilize when they produce digital, multimodal texts in the classroom. The Learning by Design framework is used as an analytic tool to theorize how students learn when they engaged in a specific domain of learning – digital media production.

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.

Director independence or decision bias? An investigation of alternative sources of agency costs in board decision making

Research into boards traditionally focuses on independent monitoring of management, with studies focused on the effect of board independence on firm performance. This thesis aims to broaden the research tradition by consolidating prior research and investigating how agents may circumvent independent monitoring. Meta-analysis of previous board independence-firm performance studies indicated no systematic relationship between board independence and firm performance. Next, a series of experiments demonstrated that the presentation of recommendations to directors may bias decision making irrespective of other information presented and the independence of the decision maker. Together, results suggest that independence may be less important than the agent's motivation to misdirect the monitoring process.

A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

On the leakage resilience of secure channel establishment

Secure communication channels are typically constructed from an authenticated key exchange (AKE) protocol, which authenticates the communicating parties and establishes shared secret keys, and a secure data transmission layer, which uses the secret keys to encrypt data. We address the partial leakage of communicating parties' long-term secret keys due to various side-channel attacks, and the partial leakage of plaintext due to data compression. Both issues can negatively affect the security of channel establishment and data transmission. In this work, we advance the modelling of security for AKE protocols by considering more granular partial leakage of parties' long-term secrets. We present generic and concrete constructions of two-pass leakage-resilient key exchange protocols that are secure in the proposed security models. We also examine two techniques--heuristic separation of secrets and fixed-dictionary compression--for enabling compression while protecting high-value secrets.

Open-pit block sequencing optimization: A mathematical model and solution technique

This study presents a comprehensive mathematical formulation model for a short-term open-pit mine block sequencing problem, which considers nearly all relevant technical aspects in open-pit mining. The proposed model aims to obtain the optimum extraction sequences of the original-size (smallest) blocks over short time intervals and in the presence of real-life constraints, including precedence relationship, machine capacity, grade requirements, processing demands and stockpile management. A hybrid branch-and-bound and simulated annealing algorithm is developed to solve the problem. Computational experiments show that the proposed methodology is a promising way to provide quantitative recommendations for mine planning and scheduling engineers.

An operational-level multi-stage mine production timetabling model for optimally synchronising drilling, blasting and excavating operations

This paper proposes a new multi-stage mine production timetabling (MMPT) model to optimise open-pit mine production operations including drilling, blasting and excavating under real-time mining constraints. The MMPT problem is formulated as a mixed integer programming model and can be optimally solved for small-size MMPT instances by IBM ILOG-CPLEX. Due to NP-hardness, an improved shifting-bottleneck-procedure algorithm based on the extended disjunctive graph is developed to solve large-size MMPT instances in an effective and efficient way. Extensive computational experiments are presented to validate the proposed algorithm that is able to efficiently obtain the near-optimal operational timetable of mining equipment units. The advantages are indicated by sensitivity analysis under various real-life scenarios. The proposed MMPT methodology is promising to be implemented as a tool for mining industry because it is straightforwardly modelled as a standard scheduling model, efficiently solved by the heuristic algorithm, and flexibly expanded by adopting additional industrial constraints.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

Cost-efficient virtual machine management in data centers

Virtual Machine (VM) management is an obvious need in today's data centers for various management activities and is accomplished in two phases— finding an optimal VM placement plan and implementing that placement through live VM migrations. These phases result in two research problems— VM placement problem (VMPP) and VM migration scheduling problem (VMMSP). This research proposes and develops several evolutionary algorithms and heuristic algorithms to address the VMPP and VMMSP. Experimental results show the effectiveness and scalability of the proposed algorithms. Finally, a VM management framework has been proposed and developed to automate the VM management activity in cost-efficient way.