949 resultados para inductive reasoning


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The exact phenotype of human periodontal ligament cells (hPDLCs) remains a controversial area. Basic fibroblast growth factor (FGF‑2) exhibits various functions and its effect on hPDLCs is also controversial. Therefore, the present study examined the effect of FGF‑2 on the growth and osteoblastic phenotype of hPDLCs with or without osteogenic inducers (dexamethasone and β‑glycerophosphate). FGF‑2 was added to defined growth culture medium and osteogenic inductive culture medium. Cell proliferation, osteogenic differentiation and mineralization were measured. The selected differentiation markers, Runx2, collagen type Ⅰ, α1 (Col1a1), osteocalcin (OCN) and epidermal growth factor receptor (EGFR), were investigated by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). Runx2 and OCN protein expression was measured by western blotting. FGF‑2 significantly increased the proliferation of hPDLCs, but did not affect alkaline phosphatase activity. RT‑qPCR analysis revealed enhanced mRNA expression of Runx2, OCN and EGFR, but suppressed Col1a1 gene expression in the absence of osteogenic inducers, whereas all these gene levels had no clear trend in their presence. The Runx2 protein expression was clearly increased, but the OCN protein level showed no evident trend. The mineralization assay demonstrated that FGF‑2 inhibited mineralized matrix deposition with osteogenic inducers. These results suggested that FGF‑2 induces the growth of immature hPDLCs, which is a competitive inhibitor of epithelial downgrowth, and suppresses their differentiation into mineralized tissue by affecting Runx2 expression. Therefore, this may lead to the acceleration of periodontal regeneration.

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This article begins with the premise that morality is an intrinsic, although often invisible, aspect of everyday social action. Drawn from a corpus of fifty audiorecorded telephone calls to Kids Helpline, an Australian helpline for children and young people, we examine one call to show how the young caller and counsellor co-construct ‘morality-in-action’. Ethnomethodological understandings and, in particular, Sacks’ (1992) description of ‘Class 2’ rules and infractions show how an adolescent caller and counsellor collaboratively assemble moral versions of the caller. In puzzling out possible motives, the caller and counsellor can be seen to be attending to the implications of different moral versions of the caller. This attribution of motives is moral work in action, with motives contingently assembled, displayed and evaluated, with such work understood as displays of moral reasoning. The counselling call makes visible the counsellor’s interactional work to support and empower the client. Analysis such as this offers counsellors ways of understanding and making visible their interactional and moral work within helpline call interactions.

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Many nations are highlighting the need for a renaissance in the mathematical sciences as essential to the well-being of all citizens (e.g., Australian Academy of Science, 2006; 2010; The National Academies, 2009). Indeed, the first recommendation of The National Academies’ Rising Above the Storm (2007) was to vastly improve K–12 science and mathematics education. The subsequent report, Rising Above the Gathering Storm Two Years Later (2009), highlighted again the need to target mathematics and science from the earliest years of schooling: “It takes years or decades to build the capability to have a society that depends on science and technology . . . You need to generate the scientists and engineers, starting in elementary and middle school” (p. 9). Such pleas reflect the rapidly changing nature of problem solving and reasoning needed in today’s world, beyond the classroom. As The National Academies (2009) reported, “Today the problems are more complex than they were in the 1950s, and more global. They’ll require a new educated workforce, one that is more open, collaborative, and cross-disciplinary” (p. 19). The implications for the problem solving experiences we implement in schools are far-reaching. In this chapter, I consider problem solving and modelling in the primary school, beginning with the need to rethink the experiences we provide in the early years. I argue for a greater awareness of the learning potential of young children and the need to provide stimulating learning environments. I then focus on data modelling as a powerful means of advancing children’s statistical reasoning abilities, which they increasingly need as they navigate their data-drenched world.

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The development and maintenance of large and complex ontologies are often time-consuming and error-prone. Thus, automated ontology learning and revision have attracted intensive research interest. In data-centric applications where ontologies are designed or automatically learnt from the data, when new data instances are added that contradict to the ontology, it is often desirable to incrementally revise the ontology according to the added data. This problem can be intuitively formulated as the problem of revising a TBox by an ABox. In this paper we introduce a model-theoretic approach to such an ontology revision problem by using a novel alternative semantic characterisation of DL-Lite ontologies. We show some desired properties for our ontology revision. We have also developed an algorithm for reasoning with the ontology revision without computing the revision result. The algorithm is efficient as its computational complexity is in coNP in the worst case and in PTIME when the size of the new data is bounded.

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Asking why is an important foundation of inquiry and fundamental to the development of reasoning skills and learning. Despite this, and despite the relentless and often disruptive nature of innovations in information and communications technology (ICT), sophisticated tools that directly support this basic act of learning appear to be undeveloped, not yet recognized, or in the very early stages of development. Why is this so? To this question, there is no single factual answer. In response, however, plausible explanations and further questions arise, and such responses are shown to be typical consequences of why-questioning. A range of contemporary scenarios are presented to highlight the problem. Consideration of the various inputs into the evolution of digital learning is introduced to provide historical context and this serves to situate further discussion regarding innovation that supports inquiry-based learning. This theme is further contextualized by narratives on openness in education, in which openness is also shown to be an evolving construct. Explanatory and descriptive contents are differentiated in order to scope out the kinds of digital tools that might support inquiry instigated by why-questioning and which move beyond the search paradigm. Probing why from a linguistic perspective reveals versatile and ambiguous semantics. The why dimension—asking, learning, knowing, understanding, and explaining why—is introduced as a construct that highlights challenges and opportunities for ICT innovation. By linking reflective practice and dialogue with cognitive engagement, this chapter points to specific frontiers for the design and development of digital learning tools, frontiers in which inquiry may find new openings for support.

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Moral vitalism refers to a tendency to view good and evil as actual forces that can influence people and events. We introduce a scale designed to assess the belief in moral vitalism. High scorers on the scale endorse items such as “There are underlying forces of good and evil in this world”. After establishing the reliability and criterion validity of the scale (Studies 1, 2a, 2b), we examined the predictive validity of the moral vitalism scale, showing that “moral vitalists” worry about being possessed by evil (Study 3), being contaminated through contact with evil people (Study 4), and forfeiting their own mental purity (Study 5). We discuss the nature of moral vitalism and the implications of the construct for understanding the role of metaphysical lay theories about the nature of good and evil in moral reasoning.

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Reflective thinking is an important skill in psychology, both as a tool in the therapeutic process and in professional development. The adapted 4Rs Model provides reflective writing structure and highlights the importance of Reporting & Responding, Relating, Reasoning and Reconstructing to students who are new to writing reflections. This chapter presents a case in which the 4Rs model (modified from the 5Rs model in Chap. 2 of this edition) was adopted to support reflective writing skills of undergraduate psychology students in a first year unit and in a final year unit. Although all students reflected on their learning within the units, the support activities leading up to the reflective tasks were adjusted to account for differences in the abilities of the cohorts and the focus of the units. In an evaluation survey, both groups of students endorsed statements about the importance of reflections and the utility of using the model. First year students also reported some difficulties understanding the 4Rs. This chapter will explore how first and final year students can be supported to develop reflection skills through scaffolding and modification of the same approaches and model.

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This commentary offers a feminist analysis of relocation cases through the lens of U v U [2002] HCA 36, and with reference to the re-written judgment for the Australian Feminist Judgments project. First, the commentary considers the gendered nature of relocation cases, and analyses aspects of the reasoning and outcome of U v U that are of concern from a feminist perspective. Second, the commentary discusses how the re-written judgment addresses these concerns, thereby offering a feminist judgment on the issue of relocation in family law.

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For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.

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Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

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Background Over half of the residents in long-term care have a diagnosis of dementia. Maintaining quality of life is important, as there is no cure for dementia. Quality of life may be used as a benchmark for caregiving, and can help to enhance respect for the person with dementia and to improve care provision. The purpose of this study was to describe quality of life as reported by people living with dementia in long-term care in terms of the influencers of, as well as the strategies needed, to improve quality of life. Methods A descriptive exploratory approach. A subsample of twelve residents across two Australian states from a national quantitative study on quality of life was interviewed. Data were analysed thematically from a realist perspective. The approach to the thematic analysis was inductive and data-driven. Results Three themes emerged in relation to influencers and strategies related to quality of life: (a) maintaining independence; (b) having something to do, and; (c) the importance of social interaction. Conclusions The findings highlight the importance of understanding individual resident needs and consideration of the complexity of living in large group living situations, in particular in regard to resident decision-making.

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The increasing amount of information that is annotated against standardised semantic resources offers opportunities to incorporate sophisticated levels of reasoning, or inference, into the retrieval process. In this position paper, we reflect on the need to incorporate semantic inference into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testing inference mechanisms to augment retrieval. The medical domain offers a plethora of carefully curated, structured, semantic resources, along with well established entity extraction and linking tools, and search topics that intuitively require a number of different inferential processes (e.g., conceptual similarity, conceptual implication, etc.). We argue that integrating semantic inference in information retrieval has the potential to uncover a large amount of information that otherwise would be inaccessible; but inference is also risky and, if not used cautiously, can harm retrieval.

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Purpose: Many haematological cancer survivors report long-term physiological and psychosocial effects beyond treatment completion. These survivors continue to experience impaired quality of life (QoL) as a result of their disease and aggressive treatment. As key members of the multidisciplinary team, the purpose of this study is to examine the insights of cancer nurses to inform future developments in survivorship care provision. Methods: Open text qualitative responses from two prospective Australian cross-sectional surveys of nurses (n=136) caring for patients with haematological cancer. Data were analysed thematically, using an inductive approach to identify themes. Results: This study has identified a number of issues that nurses perceive as barriers to quality survivorship care provision. Two main themes were identified; the first relating to the challenges nurses face in providing care (‘care challenges’), and the second relating to the challenges of providing survivorship care within contemporary health care systems (‘system challenges’). Conclusions: Cancer nurses perceive the nature of haematological cancer and its treatment, and of the health care system itself, as barriers to the provision of quality survivorship care. Care challenges such as the lack of a standard treatment path and the relapsing or remitting nature of haematological cancers may be somewhat intractable, but system challenges relating to clearly defining and delineating professional responsibilities and exchanging information with other clinicians are not. Implications for Cancer Survivors: Addressing the issues identified will facilitate cancer nurses’ provision of survivorship care, and help address haematological survivors’ needs with regard to the physical and psychosocial consequences of their cancer and treatment.

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What type of probability theory best describes the way humans make judgments under uncertainty and decisions under conflict? Although rational models of cognition have become prominent and have achieved much success, they adhere to the laws of classical probability theory despite the fact that human reasoning does not always conform to these laws. For this reason we have seen the recent emergence of models based on an alternative probabilistic framework drawn from quantum theory. These quantum models show promise in addressing cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory. This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach.

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We propose an architecture for a rule-based online management systems (RuleOMS). Typically, many domain areas face the problem that stakeholders maintain databases of their business core information and they have to take decisions or create reports according to guidelines, policies or regulations. To address this issue we propose the integration of databases, in particular relational databases, with a logic reasoner and rule engine. We argue that defeasible logic is an appropriate formalism to model rules, in particular when the rules are meant to model regulations. The resulting RuleOMS provides an efficient and flexible solution to the problem at hand using defeasible inference. A case study of an online child care management system is used to illustrate the proposed architecture.