Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Current rural valuation practice : a survey of valuers and agribusiness managers on farm management and sustainable rural land use

Over the past 20 years the nature of rural valuation practice has required most rural valuers to undertake studies in both agriculture (farm management) and valuation, especially if carrying out valuation work for financial institutions. The additional farm financial and management information obtained by rural valuers exceeds that level of information required to value commercial, retail and industrial by the capitalisation of net rent/profit valuation method and is very similar to the level of information required for the valuation of commercial and retail property by the Discounted Cash Flow valuation method. On this basis the valuers specialising in rural valuation practice have the necessary skills and information to value rural properties by an income valuation method, which can focus on the long term environmental and economic sustainability of the property being valued. This paper will review the results of an extensive survey carried out by rural property valuers in Australia, in relation to the impact of farm management on rural property values and sustainable rural land use. A particular focus of the research relates to the increased awareness of the problems of rural land degradation in Australia and the subsequent impact such problems have on the productivity of rural land. These problems of sustainable land use have resulted in the need to develop an approach to rural valuation practice that allows the valuer to factor the past management practices on the subject rural property into the actual valuation figure. An analysis of the past farm management and the inclusion of this data into the valuation methodology provides a much more reliable indication of farm sustainable economic value than the existing direct comparison valuation methodology.

Current rural valuation practice : a survey of NSW valuers and agribusiness managers

Despite the advances that have been made in relation to the valuation of Commercial, Industrial and retail property, there has not been the same progress in relation to the valuation of rural property. Although number of rural property valuations also require the valuer to carry out a full analysis of the economic performance of the farming operations, as well as the long term environmental viability of the farm, this information is rarely used to assess the value of the property, nor is it even used for a secondary valuation method. Over the past 20 years the nature of rural valuation practice has required most rural valuers to undertake studies in both agriculture (farm management) and valuation, especially if carrying out valuation work for financial institutions. The additional farm financial and management information obtained by rural valuers exceeds that level of information required to value commercial, retail and industrial by the capitalisation of net rent/profit valuation method and is very similar to the level of information required for the valuation of commercial and retail property by the Discounted Cash Flow valuation method. On this basis the valuers specialising in rural valuation practice have the necessary skills and information to value rural properties by an income valuation method. Although the direct comparison method of valuation has been sufficient in the past to value rural properties the future use of the method as the main valuation method is limited and valuers need to adopt an income valuation method as at least a secondary valuation method to overcome the problems associated with the use of direct comparison as the only rural property valuation method, especially in view of the impact that farm technical, financial and environmental .management can have on rural property values. This paper will review the results of an extensive survey carried out by rural property valuers and agribusiness managers in NSW, in relation to the impact of farm management on rural property values and rural property valuation practice.

Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

A Numerical Solution Using an Adaptively Preconditioned Lanczos Method for a Class of Linear Systems Related with the Fractional Poisson Equation

This study considers the solution of a class of linear systems related with the fractional Poisson equation (FPE) (−∇2)α/2φ=g(x,y) with nonhomogeneous boundary conditions on a bounded domain. A numerical approximation to FPE is derived using a matrix representation of the Laplacian to generate a linear system of equations with its matrix A raised to the fractional power α/2. The solution of the linear system then requires the action of the matrix function f(A)=A−α/2 on a vector b. For large, sparse, and symmetric positive definite matrices, the Lanczos approximation generates f(A)b≈β0Vmf(Tm)e1. This method works well when both the analytic grade of A with respect to b and the residual for the linear system are sufficiently small. Memory constraints often require restarting the Lanczos decomposition; however this is not straightforward in the context of matrix function approximation. In this paper, we use the idea of thick-restart and adaptive preconditioning for solving linear systems to improve convergence of the Lanczos approximation. We give an error bound for the new method and illustrate its role in solving FPE. Numerical results are provided to gauge the performance of the proposed method relative to exact analytic solutions.

Within- and between-day repeatability and variability in children's physiological responses during submaximal treadmill exercise

The purpose of this study was to verify within- and between-day repeatability and variability in children's oxygen uptake (VO^sub 2^), gross economy (GE; VO^sub 2^ divided by speed) and heart rate (HR) during treadmill walking based on self-selected speed (SS). Fourteen children (10.1 ± 1.4 years) undertook three testing sessions over 2 days in which four walking speeds, including SS were tested. Within- and between-day repeatability were assessed using the Bland and Altman method, and coefficients of variability (CV) were determined for each child across exercise bouts and averaged to obtain a mean group CV value for VO^sub 2^, GE, and HR per speed. Repeated measures analysis of variance showed no statistically significant differences in within- or between-day CV for VO^sub 2^, GE, or HR at any speed. Repeatability within- and between-day for VO^sub 2^, GE, and HR for all speeds was verified. These results suggest that submaximal VO^sub 2^ during treadmill walking is stable and reproducible at a range of speeds based on children's SS.

Stability and convergence of a new explicit finite-difference approximation for the variable-order nonlinear fractional diffusion equation

In this paper, we consider the variable-order nonlinear fractional diffusion equation View the MathML source where xRα(x,t) is a generalized Riesz fractional derivative of variable order View the MathML source and the nonlinear reaction term f(u,x,t) satisfies the Lipschitz condition |f(u1,x,t)-f(u2,x,t)|less-than-or-equals, slantL|u1-u2|. A new explicit finite-difference approximation is introduced. The convergence and stability of this approximation are proved. Finally, some numerical examples are provided to show that this method is computationally efficient. The proposed method and techniques are applicable to other variable-order nonlinear fractional differential equations.

Industrial playwriting : forms, strategies, and methods for creative production

This study, in its exploration of the attached play scripts and their method of development, evaluates the forms, strategies, and methods of an organised model of formalised playwriting. Through the examination, reflection and reaction to a perceived crisis in playwriting in the Australian theatre sector, the notion of Industrial Playwriting is arrived at: a practice whereby plays are designed and constructed, and where the process of writing becomes central to the efficient creation of new work and the improvement of the writer’s skill and knowledge base. Using a practice-led methodology and action research the study examines a system of play construction appropriate to and addressing the challenges of the contemporary Australian theatre sector. Specifically, using the action research methodology known as design-based research a conceptual framework was constructed to form the basis of the notion of Industrial Playwriting. From this two plays were constructed using a case study method and the process recorded and used to create a practical, step-by-step system of Industrial Playwriting. In the creative practice of manufacturing a single authored play, and then a group-devised play, Industrial Playwriting was tested and found to also offer a valid alternative approach to playwriting in the training of new and even emerging playwrights. Finally, it offered insight into how Industrial Playwriting could be used to greatly facilitate theatre companies’ ongoing need to have access to new writers and new Australian works, and how it might form the basis of a cost effective writer development model. This study of the methods of formalised writing as a means to confront some of the challenges of the Australian theatre sector, the practice of playwriting and the history associated with it, makes an original and important contribution to contemporary playwriting practice.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Road network extraction with new vectorization and pruning from high-resolution RS images

With the increasing resolution of remote sensing images, road network can be displayed as continuous and homogeneity regions with a certain width rather than traditional thin lines. Therefore, road network extraction from large scale images refers to reliable road surface detection instead of road line extraction. In this paper, a novel automatic road network detection approach based on the combination of homogram segmentation and mathematical morphology is proposed, which includes three main steps: (i) the image is classified based on homogram segmentation to roughly identify the road network regions; (ii) the morphological opening and closing is employed to fill tiny holes and filter out small road branches; and (iii) the extracted road surface is further thinned by a thinning approach, pruned by a proposed method and finally simplified with Douglas-Peucker algorithm. Lastly, the results from some QuickBird images and aerial photos demonstrate the correctness and efficiency of the proposed process.

Resistance : re-imagining innovation in higher education teaching and learning

The call to innovate is ubiquitous across the Australian educational policy context. The claims of innovative practices and environments that occur frequently in university mission statements, strategic plans and marketing literature suggest that this exhortation to innovate appears to have been taken up enthusiastically by the university sector. Throughout the history of universities, a range of reported deficiencies of higher education have worked to produce a notion of crisis. At present, it would seem that innovation is positioned as the solution to the notion of crisis. This thesis is an inquiry into how the insistence on innovation works to both enable and constrain teaching and learning practices in Australian universities. Alongside the interplay between innovation and crisis is the link between resistance and innovation, a link which remains largely unproblematized in the scholarly literature. This thesis works to locate and unsettle understandings of a relationship between innovation and Australian higher education. The aim of this inquiry is to generate new understandings of what counts as innovation within this context and how innovation is enacted. The thesis draws on a number of postmodernist theorists, whose works have informed firstly the research method, and then the analysis and findings. Firstly, there is an assumption that power is capillary and works through discourse to enact power relations which shape certain truths (Foucault, 1990). Secondly, this research scrutinised language practices which frame the capacity for individuals to act, alongside the language practices which encourage an individual to adopt certain attitudes and actions as one’s own (Foucault, 1988). Thirdly, innovation talk is read in this thesis as an example of needs talk, that is, as a medium through which what is considered domestic, political or economic is made and contested (Fraser, 1989). Fourthly, relationships between and within discourses were identified and analysed beyond cause and effect descriptions, and more productively considered to be in a constant state of becoming (Deleuze, 1987). Finally, the use of ironic research methods assisted in producing alternate configurations of innovation talk which are useful and new (Rorty, 1989). The theoretical assumptions which underpin this thesis inform a document analysis methodology, used to examine how certain texts work to shape the ways in which innovation is constructed. The data consisted of three Federal higher education funding policies selected on the rationale that these documents, as opposed to state or locally based policy and legislation, represent the only shared policy context for all Australian universities. The analysis first provided a modernist reading of the three documents, and this was followed by postmodernist readings of these same policy documents. The modernist reading worked to locate and describe the current truths about innovation. The historical context in which the policy was produced as well as the textual features of the document itself were important to this reading. In the first modernist reading, the binaries involved in producing proper and improper notions of innovation were described and analysed. In the process of the modernist analysis and the subsequent location of binary organisation, a number of conceptual collisions were identified, and these sites of struggle were revisited, through the application of a postmodernist reading. By applying the theories of Rorty (1989) and Fraser (1989) it became possible to not treat these sites as contradictory and requiring resolution, but rather as spaces in which binary tensions are necessary and productive. This postmodernist reading constructed new spaces for refusing and resisting dominant discourses of innovation which value only certain kinds of teaching and learning practices. By exploring a number of ironic language practices found within the policies, this thesis proposes an alternative way of thinking about what counts as innovation and how it happens. The new readings of innovation made possible through the work of this thesis were in response to a suite of enduring, inter-related questions – what counts as innovation?, who or what supports innovation?, how does innovation occur?, and who are the innovators?. The truths presented in response to these questions were treated as the language practices which constitute a dominant discourse of innovation talk. The collisions that occur within these truths were the contested sites which were of most interest for the analysis. The thesis concludes by presenting a theoretical blueprint which works to shift the boundaries of what counts as innovation and how it happens in a manner which is productive, inclusive and powerful. This blueprint forms the foundation upon which a number of recommendations are made for both my own professional practice and broader contexts. In keeping with the conceptual tone of this study, these recommendations are a suite of new questions which focus attention on the boundaries of innovation talk as an attempt to re-configure what is valued about teaching and learning at university.

Stigma reported by nurses related to those experiencing drug and alcohol dependency : a phemonenological study

Alcohol and drug dependency is a widespread health and social issue encountered by registered nurses in contemporary practice. A study aiming to describe the experiences of registered nurses working in an alcohol and drug unit in South East Queensland was implemented. Data were analysed via Giorgi’s phenomenological method and an unexpected but significant finding highlighted the frustration felt by registered nurses regarding experiences of stigma they identified in their daily work encounters. Secondary analysis confirmed the phenomenon of stigma with three themes: (1) inappropriate judgement; (2) advocacy; and (3) education. Resultantly, findings concluded registered nurses’ working in this field need to become advocates for their clients, ensuring professional conduct is upheld at all times. This paper recommends that stigma could be addressed by incorporating alcohol and other drug dependency subjects and clinical placements into the curriculum of the Bachelor of Nursing degrees, and in-services for all practising registered nurses.

Role of low affinity beta1-adrenergic receptors in normal and diseased hearts

ROLE OF LOW AFFINITY β1-ADRENERGIC RECEPTOR IN NORMAL AND DISEASED HEARTS Background: The β1-adrenergic receptor (AR) has at least two binding sites, 1HAR and 1LAR (high and low affinity site of the 1AR respectively) which cause cardiostimulation. Some β-blockers, for example (-)-pindolol and (-)-CGP 12177 can activate β1LAR at higher concentrations than those required to block β1HAR. While β1HAR can be blocked by all clinically used β-blockers, β1LAR is relatively resistant to blockade. Thus, chronic β1LAR activation may occur in the setting of β-blocker therapy, thereby mediating persistent βAR signaling. Thus, it is important to determine the potential significance of β1LAR in vivo, particularly in disease settings. Method and result: C57Bl/6 male mice were used. Chronic (4 weeks) β1LAR activation was achieved by treatment with (-)-CGP12177 via osmotic minipump. Cardiac function was assessed by echocardiography and catheterization. (-)-CGP12177 treatment in healthy mice increased heart rate and left ventricular (LV) contractility without detectable LV remodelling or hypertrophy. In mice subjected to an 8-week period of aorta banding, (-)-CGP12177 treatment given during 4-8 weeks led to a positive inotropic effect. (-)-CGP12177 treatment exacerbated LV remodelling indicated by a worsening of LV hypertrophy by ??% (estimated by weight, wall thickness, cardiomyocyte size) and interstitial/perivascular fibrosis (by histology). Importantly, (-)-CGP12177 treatment to aorta banded mice exacerbated cardiac expression of hypertrophic, fibrogenic and inflammatory genes (all p<0.05 vs. non-treated control with aorta banding).. Conclusion: β1LAR activation provides functional support to the heart, in both normal and diseased (pressure overload) settings. Sustained β1LAR activation in the diseased heart exacerbates LV remodelling and therefore may promote disease progression from compensatory hypertrophy to heart failure. Word count: 270

Longing to belong : social inclusion and wellbeing among youth with refugee backgrounds in the first three years in Melbourne, Australia

For young people with refugee backgrounds, establishing a sense of belonging to their family and community, and to their country of resettlement is essential for wellbeing. This paper describes the psychosocial factors associated with subjective health and wellbeing outcomes among a cohort of 97 refugee youth (aged 11-19) during their first three years in Melbourne, Australia. The findings reported here are drawn from the Good Starts Study, a longitudinal investigation of settlement and wellbeing among refugee youth conducted between 2004 and 2008. The overall aim of Good Starts was to identify the psychosocial factors that assist youth with refugee backgrounds in making a good start in their new country. A particular focus was on key transitions: from pre-arrival to Australia, from the language school to mainstream school, and from mainstream school to higher education or to the workforce. Good Starts used a mix of both method and theory from anthropology and social epidemiology. Using standardized measures of wellbeing and generalised estimating equations to model the predictors of wellbeing over time, this paper reports that key factors strongly associated with wellbeing outcomes are those that can be described as indicators of belonging e the most important being subjective social status in the broader Australian community, perceived discrimination and bullying. We argue that settlement specific policies and programs can ultimately be effective if embedded within a broader socially inclusive society - one that offers real opportunities for youth with refugee backgrounds to flourish.