Critical reflections on the use of participatory methodologies to build evaluation capacities in international development organisations

There has been a greater focus on strengthening evaluation capacity building (ECB) within development organisations in recent years. This can be attributed in part to the growing appreciation of the value of participatory and collaborative forms of evaluation. Evaluation is increasingly seen as an ongoing learning process and an important means of strengthening capacity and improving organisational performance (Horton et al., 2003:7). While there are many benefits of using participatory methodologies in ECB projects, our experiences and a review of the literature in this area highlight the many challenges, issues and contradictions that can affect the success of such ECB efforts. We discuss these issues, drawing on our learnings from the ongoing participatory action research (PAR) project 'Assessing Communication for Social Change’ (AC4SC). This four year project, which began in 2007, is a collaboration between communication and development academics and evaluation specialists from two Australian universities and communication for development practitioners and monitoring and evaluation (M&E) staff in the NGO Equal Access Nepal (EAN). The aim is to develop, implement, and evaluate a participatory methodology for assessing the social change impacts of community radio programs produced by EAN. It builds on previous projects that used ethnographic action research (EAR) methodology (Tacchi et al., 2007).

Critical reflections on the use of participatory methodologies to build evaluation capacities in international development organisations

"In this chapter the authors present a critique of Participatory Evaluation as worked in development projects, in this case, in Nepal. The article works between established claims that Participatory Evaluation builds capacity at programmatic and organisational levels, and the specific experiences of these claims in the authors’ current work. They highlight the need to address key difficulties such as high turn-over of staff and resulting loss of capacity to engage in Participatory Evaluation, and the difficulty of communication between academic as compared with local practical wisdoms. A key issue is the challenge of addressing the inevitable issues of power inequities that such approaches encounter. While Participatory Evaluation has been around for some time, it has only enjoyed more widespread recognition of its value in comparatively recent times, with its uptake in international development environments. To this extent, the practice is still in its early stages of development, and Jo, June and Michael’s work contributes to strengthening and more comprehensively understanding it. With regard to the meta-theme of this publication, this chapter is an example of how context not only influences the methodology to be used and the praxis of how it is to be used, but contributes to early explication of the core nature of an emerging methodology."

The Performance Nexus - A framework for improving energy efficiency in existing commercial buildings by considering a whole-of-building approach

Efforts to reduce carbon emissions in the buildings sector have been focused on encouraging green design, construction and building operation; however, the business case is not very compelling if considering the energy cost savings alone. In recent years green building has been driven by a sense that it will improve the productivity of occupants,i something with much greater economic returns than energy savings. Reducing energy demand in green commercial buildings in a way that encourages greater productivity is not yet well understood as it involves a set of complex and interdependent factors. This paper outlines an investigation into these factors and focuses on better understanding the performance of and interaction between: design elements, internal environmental quality, occupant experience, tenant/leasing agreements, and building regulation and management. In doing so the paper presents a framework for improving energy efficiency in existing commercial buildings by considering a range of interconnected and synergistic elements.

Thermodynamic modeling of ethanol fumigation in a diesel engine

Due to rapidly diminishing international supplies of fossil fuels, such as petroleum and diesel, the cost of fuel is constantly increasing, leading to higher costs of living, as a result of the significant reliance of many industries on motor vehicles. Many technologies have been developed to replace part or all of a fossil fuel with bio-fuels. One of the dual fuel technologies is fumigation of ethanol in diesel engines, which injects ethanol into the intake air stream of the engine. The advantage of this is that it avoids any costly modification of the engine high pressure diesel injection system, while reducing the volume of diesel required and potentially increasing the power output and efficiency. This paper investigates the performance of a diesel engine, converted to implement ethanol fumigation. The project will use both existing experimental data, along with generating computer modeled results using the program AVL Boost. The data from both experiments and the numerical simulation indicate desirable results for the peak pressure and the indicated mean effective pressure (IMEP). Increase in ethanol substitution resulted in elevated combustion pressure and an increase in the IMEP, while the variation of ethanol injection location resulted in negligible change. These increases in cylinder pressure led to a higher work output and total efficiency in the engine as the ethanol substitution was increased. In comparing the numerical and experimental results, the simulation showed a slight elevation, due to the inaccuracies in the heat release models. Future work is required to improve the combustion model and investigate the effect of the variation of the location of ethanol injection.

Numerical modelling of porous injection in a radical farming scramjet

A numerical investigation of the behaviour of fuel injection through a porous surface in an inlet-fuelled, radial-farming scramjet is presented. The performance of porous fuel injection is compared to discrete port hole injection at an equivalence ratio of φ ≈ 0.4 for both cases. The comparison is performed at a Mach 6.5 flow condition with a total specific enthalpy of 4.3 MJ/kg. The numerical results are compared to experiments performed in the T4 shock tunnel where available. The presented results demonstrate for the first time, that porous fuel injection has the potential to outperform port hole injectors in scramjet engines in terms of fuel-air mixing, ignition delays and achievable combustion efficiencies despite reduced fuel penetration heights.

A new grouping genetic algorithm for the MapReduce placement problem in cloud computing

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. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NPcomplete. Thus, in this paper we propose a new grouping genetic algorithm for the mappers/reducers placement problem in cloud computing. Compared with the original one, our grouping genetic algorithm uses an innovative coding scheme and also eliminates the inversion operator which is an essential operator in the original grouping genetic algorithm. The new grouping genetic algorithm is evaluated by experiments and the experimental results show that it is much more efficient than four popular algorithms for the problem, including the original grouping genetic algorithm.

Composite SaaS scaling in cloud computing using a hybrid genetic algorithm

A Software-as-a-Service or SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. Components in a composite SaaS may need to be scaled – replicated or deleted, to accommodate the user’s load. It may not be necessary to replicate all components of the SaaS, as some components can be shared by other instances. On the other hand, when the load is low, some of the instances may need to be deleted to avoid resource underutilisation. Thus, it is important to determine which components are to be scaled such that the performance of the SaaS is still maintained. Extensive research on the SaaS resource management in Cloud has not yet addressed the challenges of scaling process for composite SaaS. Therefore, a hybrid genetic algorithm is proposed in which it utilises the problem’s knowledge and explores the best combination of scaling plan for the components. Experimental results demonstrate that the proposed algorithm outperforms existing heuristic-based solutions.

Introduction to the first international workshop on process management for highly dynamic and pervasive scenarios(PM4HDPS 2008)

Nowadays, process management systems (PMSs) are widely used in many business scenarios, e.g. by government agencies, by insurance companies, and by banks. Despite this widespread usage, the typical application of such systems is predominantly in the context of static scenarios, instead of pervasive and highly dynamic scenarios. Nevertheless, pervasive and highly dynamic scenarios could also benefit from the use of PMSs.

Engineering education for sustainable development : a review of international progress

Since the late 1980s there have been increasing calls around the world for embedding sustainability content throughout engineering curricula, particularly over the past decade. However in general there has been little by way of strategic or systematic integration within programs offered by higher education institutions(HEIs). Responding to a growing awareness towards the issues surrounding sustainability, a number of professional engineering institutions (PEIs) internationally have placed increasing emphasis on policies and initiatives relating to the role of engineering in addressing 21st Century challenges. This has resulted in some consideration towards integrating sustainable development into engineering curricula as envisaged by accreditation guidelines. This paper provides a global overview of such accreditation developments, highlighting emerging sustainability competencies (or ‘graduate attributes’) and places these in the context of relevant PEI declarations, initiatives, policies, codes of ethics and guideline publications. The paper concludes by calling for urgent action by PEIs, including strategic accreditation initiatives that promote timely curriculum renewal towards EESD.

Promoting resilience and effective workplace functioning in international students in health courses

The purpose of this project was to improve the quality of the learning experiences of international students in nursing, public health and nutrition and dietetics, both at university and in the clinical setting. The university worked in partnership with three major metropolitan health care facilities/services in Queensland to develop a framework and resources designed to promote quality work-integrated learning experiences for international students and clinical supervisory staff. The Resilience in International Student Education (RISE) model consists of student and staff workshops complemented by a purpose-built Cultural Connections for Learning (CCL) website that provides access to a wide variety of information and other learning resources. Quantitative and qualitative evaluations indicate that the approach is highly valued by participants as it promotes useful dialogue, sharing of experiences and greater understanding regarding quality learning experiences for international students in the health workplace. It provides an ideal springboard for promoting collaboration between international students and clinical supervisors in the workplace. The resources developed have the potential to enhance student learning as well as clinical teaching. The challenge will be to achieve continued progress within international student education through the development of sustainable strategies to embed the program within the context of individual curricula.

Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.