917 resultados para sequential cropping
Resumo:
With the overwhelming increase in the amount of data on the web and data bases, many text mining techniques have been proposed for mining useful patterns in text documents. Extracting closed sequential patterns using the Pattern Taxonomy Model (PTM) is one of the pruning methods to remove noisy, inconsistent, and redundant patterns. However, PTM model treats each extracted pattern as whole without considering included terms, which could affect the quality of extracted patterns. This paper propose an innovative and effective method that extends the random set to accurately weigh patterns based on their distribution in the documents and their terms distribution in patterns. Then, the proposed approach will find the specific closed sequential patterns (SCSP) based on the new calculated weight. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms other state-of-the-art methods in different popular measures.
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Background Randomised controlled trials may be of limited use to evaluate the multidisciplinary and multimodal interventions required to effectively treat complex patients in routine clinical practice; pragmatic action research approaches may provide a suitable alternative. Methods A multiphase, pragmatic, action research based approach was developed to identify and overcome barriers to nutritional care in patients admitted to a metropolitan hospital hip-fracture unit. Results Four sequential action research cycles built upon baseline data including 614 acute hip-fracture inpatients and 30 purposefully sampled clinicians. Reports from Phase I identified barriers to nutrition screening and assessment. Phase II reported post-fracture protein-energy intakes and intake barriers. Phase III built on earlier results; an explanatory mixed-methods study expanded and explored additional barriers and facilitators to nutritional care. Subsequent changes to routine clinical practice were developed and implemented by the treating team between Phase III and IV. These were implemented as a new multidisciplinary, multimodal nutritional model of care. A quasi-experimental controlled, ‘before-and-after’ study was then used to compare the new model of care with an individualised nutritional care model. Engagement of the multidisciplinary team in a multiphase, pragmatic action research intervention doubled energy and protein intakes, tripled return home discharge rates, and effected a 75% reduction in nutritional deterioration during admission in a reflective cohort of hip-fracture inpatients. Conclusions This approach allowed research to be conducted as part of routine clinical practice, captured a more representative patient cohort than previously reported studies, and facilitated exploration of barriers and engagement of the multidisciplinary healthcare workers to identify and implement practical solutions. This study demonstrates substantially different findings to those previously reported, and is the first to demonstrate that multidisciplinary, multimodal nutrition care reduces intake barriers, delivers a higher proportional increase in protein and energy intake compared with baseline than other published intervention studies, and improves patient outcomes when compared with individualised nutrition care. The findings are considered highly relevant to clinical practice and have high translation validity. The authors strongly encourage the development of similar study designs to investigate complex health problems in elderly, multi-morbid patient populations as a way to evaluate and change clinical practice.
Resumo:
Monogenetic volcanoes have long been regarded as simple in nature, involving single magma batches and uncomplicated evolutions; however, recent detailed research into individual centres is challenging that assumption. Mt Rouse (Kolor) is the volumetrically largest volcano in the monogenetic Newer Volcanics Province of southeast Australia. This study presents new major, trace and Sr–Nd–Pb isotope data for samples selected on the basis of a detailed stratigraphic framework analysis of the volcanic products from Mt Rouse. The volcano is the product of three magma batches geochemically similar to Ocean–Island basalts, featuring increasing LREE enrichment with each magma batch (batches A, B and C) but no evidence of crustal contamination; the Sr–Nd–Pb isotopes define two groupings. Modelling suggests that the magmas were sourced from a zone of partial melting crossing the lithosphere–asthenosphere boundary, with batch A forming a large volume partial melt in the deep lithosphere (1.7 GPa/55.5 km); and batches B and C from similar areas within the shallow asthenosphere (1.88 GPa/61 km and 1.94 GPa/63 km, respectively). The formation and extraction of these magmas may have been due to high deformation rates in the mantle caused by edge-driven convection and asthenospheric upwelling. The lithosphere– asthenosphere boundary is important with respect to NVP volcanism. An eruption chronology involves sequential eruption of magma batches A, C and B, followed by simultaneous eruption of batches A and B. Mt Rouse is a complex polymagmatic monogenetic volcano that illustrates the complexity of monogenetic volcanism and demonstrates the importance of combining detailed stratigraphic analysis alongside systematic geochemical sampling.
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In the current regulatory climate, there is increasing expectation that law schools will be able to demonstrate students’ acquisition of learning outcomes regarding collaboration skills. We argue that this is best achieved through a stepped and structured whole-of-curriculum approach to small group learning. ‘Group work’ provides deep learning and opportunities to develop professional skills, but these benefits are not always realised for law students. An issue is that what is meant by ‘group work’ is not always clear, resulting in a learning regime that may not support the attainment of desired outcomes. This paper describes different types of ‘group work', each associated with distinct learning outcomes. It suggests that ‘group work’ as an umbrella term to describe these types is confusing, as it provides little indication to students and teachers of the type of learning that is valued and is expected to take place. ‘Small group learning’ is a preferable general descriptor. Identifying different types of small group learning allows law schools to develop and demonstrate a scaffolded, sequential and incremental approach to fostering law students’ collaboration skills. To support learning and the acquisition of higherorder skills, different types of small group learning are more appropriate at certain stages of the program. This structured approach is consistent with social cognitive theory, which suggests that with the guidance of a supportive teacher, students can develop skills and confidence in one type of activity which then enhances motivation to participate in another.
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In this commentary I reflect upon the conceptualisation of human meaning-making, utilised in the two target articles, that relies heavily on speech as the main mode of semiosis and considers time only in its chronological form. Instead I argue that human existence is embodied and lived through multiple modalities, and involves not only sequential experience of time, but also experience of emergence. In order to move towards a conception of meaning-making that takes this into account, I introduce the social-semiotic theory of multimodality (Kress 2010) and discuss notions of ‘real duration’ (Bergson 1907/1998) and ‘lived time’ (Martin-Vallas 2009). I argue that dialogical (idiographic) researchers need to develop analytic and methodological tools that allow exploring the emergence of multimodal assemblages of meaning in addition to trying to avoid the monologisation of complex dynamic dialogical phenomena.
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My concern in this commentary is the discrepancy between cultural psychologists' theoretical claims that meanings are co-constructed by, with and for individuals in ongoing social interaction, and their research practices where researcher's and research participant's meaning-making processes are separated in time into sequential turns. I argue for the need to live up to these theoretical assumptions, by making both the initial research encounter and the researcher's later interpretation process more co-constructive. I suggest making the initial research encounter more co-constructive by paying attention to these moments when the negotiated flow of interaction between researcher and research participant breaks down, for it allows the research participant's meaning-making to be traced and makes the researcher's efforts towards meaning more explicit. I propose to make the later interpretation process more co-constructive by adopting a more open-ended and dialogical way of writing that is specifically addressed to research participants and invites them to actively engage with researcher's meaning-making.
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The requirement of isolated relays is one of the prime obstacles in utilizing sequential slotted cooperative protocols for Vehicular Ad-hoc Networks (VANET). Significant research advancement has taken place to improve the diversity multiplexing trade-off (DMT) of cooperative protocols in conventional mobile networks without much attention on vehicular ad-hoc networks. We have extended the concept of sequential slotted amplify and forward (SAF) protocols in the context of urban vehicular ad-hoc networks. Multiple Input Multiple Output (MIMO) reception is used at relaying vehicular nodes to isolate the relays effectively. The proposed approach adds a pragmatic value to the sequential slotted cooperative protocols while achieving attractive performance gains in urban VANETs. We have analysed the DMT bounds and the outage probabilities of the proposed scheme. The results suggest that the proposed scheme can achieve an optimal DMT similar to the DMT upper bound of the sequential SAF. Furthermore, the outage performance of the proposed scheme outperforms the SAF protocol by 2.5 dB at a target outage probability of 10-4.
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We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles.
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This is the first study to investigate alternative fertilisation strategies to increase cereal production while reducing greenhouse gas emissions from the most common soil type in subtropical regions. The results of this research will contribute to define future farming practices to achieve global food security and mitigate climate change. The study established that introducing legumes in cropping systems is the most agronomically viable and environmentally sustainable fertilisation strategy. Importantly, this strategy can be widely adopted in subtropical regions since it is economically accessible, requires little know-how transfer and technology investment, and can be profitable in both low- and high-input cropping systems.
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The growth of APIs and Web services on the Internet, especially through larger enterprise systems increasingly being leveraged for Cloud and software-as-a-service opportunities, poses challenges for improving the efficiency of integration with these services. Interfaces of enterprise systems are typically larger, more complex and overloaded, with single operations having multiple data entities and parameter sets, supporting varying requests, and reflecting versioning across different system releases, compared to fine-grained operations of contemporary interfaces. We propose a technique to support the refactoring of service interfaces by deriving business entities and their relationships. In this paper, we focus on the behavioural aspects of service interfaces, aiming to discover the sequential dependencies of operations (otherwise known as protocol extraction) based on the entities and relationships derived. Specifically, we propose heuristics according to these relationships, and in turn, deriving permissible orders in which operations are invoked. As a result of this, service operations can be refactored on business entity CRUD lines, with explicit behavioural protocols as part of an interface definition. This supports flexible service discovery, composition and integration. A prototypical implementation and analysis of existing Web services, including those of commercial logistic systems (Fedex), are used to validate the algorithms proposed through the paper.
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In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.
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In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ.
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Background Haemodialysis nurses work in a technological environment caring for patients over a prolonged period of time leading to the development of unique nurse-patient relationships. In order to improve retention of nurses in this specialised area of nursing it is important to know the factors that affect job satisfaction, stress and burnout and understand how these experiences are conceptualised by haemodialysis nurses. Aim To explore the factors contributing to satisfaction with the work environment, job satisfaction, job stress and burnout in haemodialysis nurses in Australia and New Zealand. Method A quantitative dominant sequential explanatory mixed method design was used. Quantitative data was collected using an on-line questionnaire containing demographic questions and pre-existing instruments examining job satisfaction, stress, burnout and satisfaction with the work environment. The qualitative phase involved semi-structured interviews. Results 417 nurses completed the questionnaire. Overall, nurses were satisfied with their work environment and the job that they performed but there were stressors in the haemodialysis setting that led to high levels of burnout. Key themes emerged from the qualitative data related to the physical environment, intensity of nurse-patient relationships, workloads, and coping with death and dying. The qualitative findings also provide possible explanations for the high level of burnout identified in the quantitative findings. Conclusion Explanation of areas where specific nurse and patient outcomes were affected will support the development of appropriate interventions to sustain a work environment conducive to job satisfaction that also alleviates stress and burnout in these nurses.
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For wind farm optimizations with lands belonging to different owners, the traditional penalty method is highly dependent on the type of wind farm land division. The application of the traditional method can be cumbersome if the divisions are complex. To overcome this disadvantage, a new method is proposed in this paper for the first time. Unlike the penalty method which requires the addition of penalizing term when evaluating the fitness function, it is achieved through repairing the infeasible solutions before fitness evaluation. To assess the effectiveness of the proposed method on the optimization of wind farm, the optimizing results of different methods are compared for three different types of wind farm division. Different wind scenarios are also incorporated during optimization which includes (i) constant wind speed and wind direction; (ii) various wind speed and wind direction, and; (iii) the more realisticWeibull distribution. Results show that the performance of the new method varies for different land plots in the tested cases. Nevertheless, it is found that optimum or at least close to optimum results can be obtained with sequential land plot study using the new method for all cases. It is concluded that satisfactory results can be achieved using the proposed method. In addition, it has the advantage of flexibility in managing the wind farm design, which not only frees users to define the penalty parameter but without limitations on the wind farm division.
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This paper describes the use of exploratory focus groups to inform the development of a survey instrument in a sequential phase mixed methods study investigating differences in secondary students’ career choice capability. Five focus groups were conducted with 23 year 10 students in the state of New South Wales (NSW), Australia. Analysis of the focus group data informed the design of the instrument for the second phase of the research project: a large-scale cross-sectional survey. In this paper, we discuss the benefits of using sequential phase mixed method approaches when inquiring into complex phenomena such as human capability.