Confronting the conspiracy of silence and denial of difference for late discovery adoptive persons and donor conceived people

This article outlines the impact that a conspiracy of silence and denial of difference has had on some adopted and donor conceived persons who have been lied to or misled about their origins. Factors discussed include deceit - expressed as a central secret which undermines the fabric of a family and through distortion mystifies communication processes; the shock of discovery - often revealed accidentally and the associated sense of betrayal when this occurs; and a series of losses, for example, kinship, medical history, culture and agency which result in having to rebuild personal identity. By providing those affected with a voice, validation and vindication healing can begin. Any feelings of disregard, of betrayal of trust, of anger, frustration, sorrow or loss, need to be regarded as real, expected, and above all, a valid reaction to what has occurred. The author is a 'late discoverer' of her adoption and draws on the information from her doctoral research on the same topic which was completed in 2012.

Exploring the ethical implications of the late discovery of adoptive and donor-insemination offspring status

Some children adopted under the now discredited period of closed adoption were never told of their adoptive status until it was revealed to them in adulthood. Yet to date, this ‘late-discovery’ experience has received little research attention. Now a new generation of ‘late discoverers’ is emerging as a result of (heterosexual couple) donor insemination (DI) practices. This study of 25 late-discovery participants of either adoptive or (heterosexual couple) DI offspring status reveals ethical concerns particular to the lateness of discovery. Most of the participants were Australian, with the remainder from the UK, USA and Canada. All were asked to give an ‘open’ account of their experience, with four themes or suggestions provided on request. These accounts were added to those available in relevant publications. The analysis employed a hermeneutic phenomenological methodology and all accounts were analysed using an ethical perspective developed by Walker (2006, 2007). The main themes that emerged were: disrupted personal autonomy, betrayal of deep levels of trust and feelings of injustice and diminished self-worth. The lack of recognition of concerns particular to late discovery has resulted in late discoverers (i) feeling unable to regain a sense of personal control, (ii) significantly disrupted relationships with those closest to them and others, including community and institutions, and (iii) feelings of diminished value and self-worth.

An evaluation framework for cross-lingual link discovery

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. This framework includes topics, document collections, assessments, metrics, and a toolkit for pooling, assessment, and evaluation. The assessments are further divided into two separate sets: manual assessments performed by human assessors; and automatic assessments based on links extracted from Wikipedia itself. Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross-lingual link discovery.

Overview of the NTCIR-10 cross-lingual link discovery task

This paper presents an overview of NTCIR-10 Cross-lingual Link Discovery (CrossLink-2) task. For the task, we continued using the evaluation framework developed for the NTCIR-9 CrossLink-1 task. Overall, recommended links were evaluated at two levels (file-to-file and anchor-to-file); and system performance was evaluated with metrics: LMAP, R-Prec and P@N.

Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways.

Industrial land price and its impact on urban growth : a Chinese case study

China is experiencing rapid progress in industrialization, with its own rationale toward industrial land development based on a deliberate change from an extensive to intensive form of urban land use. One result has been concerted attempts by local government to attract foreign investment by a low industrial land price strategy, which has resulted in a disproportionally large amount of industrial land within the total urban land use structure at the expense of the urban sprawl of many cities. This paper first examines “Comparable Benchmark Price as Residential land use” (CBPR) as the theoretical basis of the low industrial land price phenomenon. Empirical findings are presented from a case study based on data from Jinyun County, China. These data are analyzed to reveal the rationale of industrial land price from 2000 to 2010 concerning the CBPR model. We then explore the causes of low industrial land prices in the form of a “Centipede Game Model”, involving two neighborhood regions as “major players” to make a set of moves (or strategies). When one of the players unilaterally reduces the land price to attract investment with the aim to maximize profits arising from the revenues generated from foreign investment and land premiums, a two-player price war begins in the form of a dynamic game, the effect of which is to produce a downward spiral of prices. In this context, the paradox of maximizing profits for each of the two players are not accomplished due to the inter-regional competition of attracted investment leading to a lose-lose situation for both sides’ in competing for land premium revenues. A short-term solution to the problem is offered involving the establishment of inter-regional cooperative partnerships. For the longer term, however, a comprehensive reform of the local financial system, more adroit regional planning and an improved means of evaluating government performance is needed to ensure the government's role in securing pubic goods is not abandoned in favor of one solely concerned with revenue generation.

Modeling electricity price events as point processes

Energy prices are highly volatile and often feature unexpected spikes. It is the aim of this paper to examine whether the occurrence of these extreme price events displays any regularities that can be captured using an econometric model. Here we treat these price events as point processes and apply Hawkes and Poisson autoregressive models to model the dynamics in the intensity of this process.We use load and meteorological information to model the time variation in the intensity of the process. The models are applied to data from the Australian wholesale electricity market, and a forecasting exercise illustrates both the usefulness of these models and their limitations when attempting to forecast the occurrence of extreme price events.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

Culture in global business transformation projects : the discovery of a grounded theory

Presently organisations engage in what is termed as Global Business Transformation Projects [GBTPs], for consolidating, innovating, transforming and restructuring their processes and business strategies while undergoing fundamental change. Culture plays an important role in global business transformation projects as these involve people of different cultural backgrounds and span across countries, industries and disciplinary boundaries. Nevertheless, there is scant empirical research on how culture is conceptualised beyond national and organisational cultures but also on how culture is to be taken into account and dealt with within global business transformation projects. This research is situated in a business context and discovers a theory that aids in describing and dealing with culture. It draws on the lived experiences of thirty-two senior management practitioners, reporting on more than sixty-one global business transformation projects in which they were actively involved. The research method used is a qualitative and interpretive one and applies a grounded theory approach, with rich data generated through interviews. In addition, vignettes were developed to illustrate the derived theoretical models. The findings from this study contribute to knowledge in multiple ways. First, it provides a holistic account of global business transformation projects that describe the construct of culture by the elements of culture types, cultural differences and cultural diversity. A typology of culture types has been developed which enlarges the view of culture beyond national and organisational culture including an industry culture, professional service firm culture and 'theme' culture. The amalgamation of the culture types instantiated in a global business transformation project compromises its project culture. Second, the empirically grounded process for managing culture in global business transformation projects integrates the stages of recognition, understanding and management as well as the enablement providing a roadmap for dealing with culture in global business transformation projects. Third, this study identified contextual variables to global business transformation projects, which provide the means of describing the environment global business transformation projects are situated, influence the construct of culture and inform the process for managing culture. Fourth, the contribution to the research method is the positioning of interview research as a strategy for data generation and the detailed documentation applying grounded theory to discover theory.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts

In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.

The impact of the clean development mechanism (CDM) on the cost price of wind power electricity : a China study

As a renewable energy source, wind power is playing an increasingly important role in China’s electricity supply. Meanwhile, China is also the world’s largest market for Clean Development Mechanism (CDM) wind power projects. Based on the data of 27 wind power projects of Inner Mongolia registered with the Executive Board of the United Nations (EB) in 2010, this paper constructs a financial model of Net Present Value (NPV) to analyze the cost of wind power electricity. A sensitivity analysis is then conducted to examine the impact of different variables with and without Certified Emission Reduction (CER) income brought about by the CDM. It is concluded that the CDM, along with static investment and annual wind electricity production, is one of the most significant factors in promoting the development of wind power in China. Additionally, wind power is envisaged as a practical proposition for competing with thermal power if the appropriate actions identified in the paper are made.

The effect of consumer education on unit price usage : an early exploratory study

Consumer awareness and usage of Unit Price (UP) information continues to hold academic interest. Originally designed as a device to enable shoppers to make comparisons between grocery products, it is argued consumers still lack a sufficient understanding of the device. Previous research has tended to focus on product choice, effect of time, and structural changes to price presentation. No studies have tested the effect of UP consumer education on grocery shopping expenditure. Supported by distributed learning theories, this is the first study to condition participants over a twenty week period, to comprehend and employ UP information while shopping. A 3x5 mixed factorial design was employed to collect data from 357 shoppers. A 3 (Control, Massed, Spaced) x 5 (Time Point: Week 0, 5, 10, 15 and 20) mixed factorial analysis of variance (ANOVA) was performed to analyse the data. Preliminary results revealed that the three groups differed in their average expenditure over the twenty weeks. The Control group remained stable across the five time points. Results indicated that both intensive (Massed) and less intensive (Spaced) exposure to UP information achieved similar results, with both group reducing average expenditure similarly by Week 5. These patterns held for twenty weeks, with conditioned groups reducing their grocery expenditure by over 10%. This research has academic value as a test of applied learning theories. We argue, retailers can attain considerable market advantages as efforts to enhance customers’ knowledge, through consumer education campaigns, can have a positive and strong impact on customer trust and goodwill toward the organisation. Hence, major practical implications for both regulators and retailers exist.