Dialogicality in Focus : Challenges to Theory, Method and Application

The phenomenon which dialogism addresses is human interaction. It enables us to conceptualise human interaction as intersubjective, symbolic, cultural, transformative and conflictual, in short, as complex. The complexity of human interaction is evident in all domains of human life, for example, in therapy, education, health intervention, communication, and coordination at all levels. A dialogical approach starts by acknowledging that the social world is perspectival, that people and groups inhabit different social realities. This book stands apart from the proliferation of recent books on dialogism, because rather than applying dialogism to this or that domain, the present volume focuses on dialogicality itself to interrogate the concepts and methods which are taken for granted in the burgeoning literature.

Identity in dialogue : identity as hyper-generalized personal sense

In this paper I propose that identity is momentary, fluid, and multiple while simultaneously providing us with a sense of sameness and continuity. Building on Valsiner’s ideas about human sense-making I suggest that we can reasonably deal with the multiplicity/unity paradox if we conceive of this process as resulting in the construction of a fuzzy field of hyper-generalized personal sense, which ordinarily functions as an implicit and unspeakable background of our everyday functioning, while being constantly re-created through momentary instances of foregrounded and explicit identity-dialogues. I illustrate the ideas put forward in the paper by analysing a case of a young woman experiencing a change in her being. Finally, in an attempt to illustrate and further develop the case I introduce a metaphor of carpet-weaving as an apposite image for thinking about identity as a process of a multiple and fragmented, yet also a united and constant being.

Strategic supply chain management factors influencing agribusiness innovation utilization

Purpose – The purpose of this paper is to examine empirically, an industry development paradox, using embryonic literature in the area of strategic supply chain management, together with innovation management literature. This study seeks to understand how, forming strategic supply chain relationships, and developing strategic supply chain capability, influences beneficial supply chain outcomes expected from utilizing industry-led innovation, in the form of electronic business solutions using the internet, in the Australian beef industry. Findings should add valuable insights to both academics and practitioners in the fields of supply chain innovation management and strategic supply chain management, and expand knowledge to current literature. Design/methodology/approach – This is a quantitative study comparing innovative and non-innovative supply chain operatives in the Australian beef industry, through factor analysis and structural equation modeling using PAWS Statistical V18 and AMOS V18 to analyze survey data from 412 respondents from the Australian beef supply chain. Findings – Key findings are that both innovative and non-innovative supply chain operators attribute supply chain synchronization as only a minor indicator of strategic supply chain capability, contrary to the literature; and they also indicate strategic supply chain capability has a minor influence in achieving beneficial outcomes from utilizing industry-led innovation. These results suggest a lack of coordination between supply chain operatives in the industry. They also suggest a lack of understanding of the benefits of developing a strategic supply chain management competence, particularly in relation to innovation agendas, and provides valuable insights as to why an industry paradox exists in terms of the level of investment in industry-led innovation, vs the level of corresponding benefit achieved. Research limitations/implications – Results are not generalized due to the single agribusiness industry studied and the single research method employed. However, this provides opportunity for further agribusiness studies in this area and also studies using alternate methods, such as qualitative, in-depth analysis of these factors and their relationships, which may confirm results or produce different results. Further, this study empirically extends existing theoretical contributions and insights into the roles of strategic supply chain management and innovation management in improving supply chain and ultimately industry performance while providing practical insights to supply chain practitioners in this and other similar agribusiness industries. Practical implications – These findings confirm results from a 2007 research (Ketchen et al., 2007) which suggests supply chain practice and teachings need to take a strategic direction in the twenty-first century. To date, competence in supply chain management has built up from functional and process orientations rather than from a strategic perspective. This study confirms that there is a need for more generalists that can integrate with various disciplines, particularly those who can understand and implement strategic supply chain management. Social implications – Possible social implications accrue through the development of responsible government policy in terms of industry supply chains. Strategic supply chain management and supply chain innovation management have impacts to the social fabric of nations through the sustainability of their industries, especially agribusiness industries which deal with food safety and security. If supply chains are now the competitive weapon of nations then funding innovation and managing their supply chain competitiveness in global markets requires a strategic approach from everyone, not just the industry participants. Originality/value – This is original empirical research, seeking to add value to embryonic and important developing literature concerned with adopting a strategic approach to supply chain management. It also seeks to add to existing literature in the area of innovation management, particularly through greater understanding of the implications of nations developing industry-wide, industry-led innovation agendas, and their ramifications to industry supply chains.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

A numerical method for the fractional Fitzhugh–Nagumo monodomain model

A fractional FitzHugh–Nagumo monodomain model with zero Dirichlet boundary conditions is presented, generalising the standard monodomain model that describes the propagation of the electrical potential in heterogeneous cardiac tissue. The model consists of a coupled fractional Riesz space nonlinear reaction-diffusion model and a system of ordinary differential equations, describing the ionic fluxes as a function of the membrane potential. We solve this model by decoupling the space-fractional partial differential equation and the system of ordinary differential equations at each time step. Thus, this means treating the fractional Riesz space nonlinear reaction-diffusion model as if the nonlinear source term is only locally Lipschitz. The fractional Riesz space nonlinear reaction-diffusion model is solved using an implicit numerical method with the shifted Grunwald–Letnikov approximation, and the stability and convergence are discussed in detail in the context of the local Lipschitz property. Some numerical examples are given to show the consistency of our computational approach.

DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials

The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate-limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high-throughput system to scrutinize massive crystallization campaigns automatically. © 2010 International Union of Crystallography Printed in Singapore-all rights reserved.

A novel ambiguity acceptance test threshold determination method with controllable failure rate

Ambiguity validation as an important procedure of integer ambiguity resolution is to test the correctness of the fixed integer ambiguity of phase measurements before being used for positioning computation. Most existing investigations on ambiguity validation focus on test statistic. How to determine the threshold more reasonably is less understood, although it is one of the most important topics in ambiguity validation. Currently, there are two threshold determination methods in the ambiguity validation procedure: the empirical approach and the fixed failure rate (FF-) approach. The empirical approach is simple but lacks of theoretical basis. The fixed failure rate approach has a rigorous probability theory basis, but it employs a more complicated procedure. This paper focuses on how to determine the threshold easily and reasonably. Both FF-ratio test and FF-difference test are investigated in this research and the extensive simulation results show that the FF-difference test can achieve comparable or even better performance than the well-known FF-ratio test. Another benefit of adopting the FF-difference test is that its threshold can be expressed as a function of integer least-squares (ILS) success rate with specified failure rate tolerance. Thus, a new threshold determination method named threshold function for the FF-difference test is proposed. The threshold function method preserves the fixed failure rate characteristic and is also easy-to-apply. The performance of the threshold function is validated with simulated data. The validation results show that with the threshold function method, the impact of the modelling error on the failure rate is less than 0.08%. Overall, the threshold function for the FF-difference test is a very promising threshold validation method and it makes the FF-approach applicable for the real-time GNSS positioning applications.

A Boundary-Oriented Chinese Segmentation Method Using NGram Mutual Information

This paper describes our participation in the Chinese word segmentation task of CIPS-SIGHAN 2010. We implemented an n-gram mutual information (NGMI) based segmentation algorithm with the mixed-up features from unsupervised, supervised and dictionarybased segmentation methods. This algorithm is also combined with a simple strategy for out-of-vocabulary (OOV) word recognition. The evaluation for both open and closed training shows encouraging results of our system. The results for OOV word recognition in closed training evaluation were however found unsatisfactory.

A novel tuning method for advanced line drop compensator and its application to response coordination of distributed generation with voltage regulating devices

Nowadays, integration of small-scale electricity generators, known as Distributed Generation (DG), into distribution networks has become increasingly popular. This tendency together with the falling price of DG units has a great potential in giving the DG a better chance to participate in voltage regulation process, in parallel with other regulating devices already available in the distribution systems. The voltage control issue turns out to be a very challenging problem for distribution engineers, since existing control coordination schemes need to be reconsidered to take into account the DG operation. In this paper, a control coordination approach is proposed, which is able to utilize the ability of the DG as a voltage regulator, and at the same time minimize the interaction of DG with another DG or other active devices, such as On-load Tap Changing Transformer (OLTC). The proposed technique has been developed based on the concepts of protection principles (magnitude grading and time grading) for response coordination of DG and other regulating devices and uses Advanced Line Drop Compensators (ALDCs) for implementation. A distribution feeder with tap changing transformer and DG units has been extracted from a practical system to test the proposed control technique. The results show that the proposed method provides an effective solution for coordination of DG with another DG or voltage regulating devices and the integration of protection principles has considerably reduced the control interaction to achieve the desired voltage correction.

Standardising and assessing digital images for use in clinical trials : a practical, reproducible method that blinds the assessor to treatment allocation

With the increasing availability of high quality digital cameras that are easily operated by the non-professional photographer, the utility of using digital images to assess endpoints in clinical research of skin lesions has growing acceptance. However, rigorous protocols and description of experiences for digital image collection and assessment are not readily available, particularly for research conducted in remote settings. We describe the development and evaluation of a protocol for digital image collection by the non-professional photographer in a remote setting research trial, together with a novel methodology for assessment of clinical outcomes by an expert panel blinded to treatment allocation.

A data mining based method for discovery of web services and their compositions

Due to the availability of huge number of web services, finding an appropriate Web service according to the requirements of a service consumer is still a challenge. Moreover, sometimes a single web service is unable to fully satisfy the requirements of the service consumer. In such cases, combinations of multiple inter-related web services can be utilised. This paper proposes a method that first utilises a semantic kernel model to find related services and then models these related Web services as nodes of a graph. An all-pair shortest-path algorithm is applied to find the best compositions of Web services that are semantically related to the service consumer requirement. The recommendation of individual and composite Web services composition for a service request is finally made. Empirical evaluation confirms that the proposed method significantly improves the accuracy of service discovery in comparison to traditional keyword-based discovery methods.

Method of symptom assessment influences cognitive, affective and somatic post-concussion-like symptom base rates

Primary objective: To investigate whether assessment method influences the type of post-concussion-like symptoms. Methods and procedures: Participants were 73 Australian undergraduate students (Mage = 24.14, SD = 8.84; 75.3% female) with no history of mild traumatic brain injury (mTBI). Participants reported symptoms experienced over the previous 2 weeks in response to an open-ended question (free report), mock interview and standardized checklist (British Columbia Post-concussion Symptom Inventory; BC-PSI). Main outcomes and results: In the free report and checklist conditions, cognitive symptoms were reported significantly less frequently than affective (free report: p < 0.001; checklist: p < 0.001) or somatic symptoms (free report: p < 0.001; checklist: p = 0.004). However, in the mock structured interview condition, cognitive and somatic symptoms were reported significantly less frequently than affective symptoms (both p < 0.001). No participants reported at least one symptom from all three domains when assessed by free report, whereas most participants did so when symptoms were assessed by a mock structured interview (75%) or checklist (90%). Conclusions: Previous studies have shown that the method used to assess symptoms affects the number reported. This study shows that the assessment method also affects the type of reported symptoms.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.